From b4631db385cb2903ac06f911f5db94325aefb972 Mon Sep 17 00:00:00 2001 From: Ruangrin L <88072261+idalr@users.noreply.github.com> Date: Thu, 25 Jan 2024 18:24:58 +0100 Subject: [PATCH 01/42] added statistics: Relation_argument_outer_token_distance.md --- .../Relation_argument_outer_token_distance.md | 156 ++++++++++++++++++ .../relation_token_distance/rtd_aae2_test.png | Bin 0 -> 13158 bytes .../rtd_aae2_train.png | Bin 0 -> 11536 bytes .../rtd_abs-glu_test.png | Bin 0 -> 12119 bytes .../rtd_abs-mix_test.png | Bin 0 -> 11333 bytes .../rtd_abs-neo_dev.png | Bin 0 -> 11332 bytes .../rtd_abs-neo_test.png | Bin 0 -> 12849 bytes .../rtd_abs-neo_train.png | Bin 0 -> 13259 bytes .../relation_token_distance/rtd_argmicro.png | Bin 0 -> 12306 bytes .../relation_token_distance/rtd_cdcp_test.png | Bin 0 -> 12076 bytes .../rtd_cdcp_train.png | Bin 0 -> 13228 bytes .../relation_token_distance/rtd_sciarg.png | Bin 0 -> 13391 bytes .../rtd_scidtb-argmin.png | Bin 0 -> 12136 bytes 13 files changed, 156 insertions(+) create mode 100644 AM_statistics/Relation_argument_outer_token_distance.md create mode 100644 AM_statistics/img/relation_token_distance/rtd_aae2_test.png create mode 100644 AM_statistics/img/relation_token_distance/rtd_aae2_train.png create mode 100644 AM_statistics/img/relation_token_distance/rtd_abs-glu_test.png create mode 100644 AM_statistics/img/relation_token_distance/rtd_abs-mix_test.png create mode 100644 AM_statistics/img/relation_token_distance/rtd_abs-neo_dev.png create mode 100644 AM_statistics/img/relation_token_distance/rtd_abs-neo_test.png create mode 100644 AM_statistics/img/relation_token_distance/rtd_abs-neo_train.png create mode 100644 AM_statistics/img/relation_token_distance/rtd_argmicro.png create mode 100644 AM_statistics/img/relation_token_distance/rtd_cdcp_test.png create mode 100644 AM_statistics/img/relation_token_distance/rtd_cdcp_train.png create mode 100644 AM_statistics/img/relation_token_distance/rtd_sciarg.png create mode 100644 AM_statistics/img/relation_token_distance/rtd_scidtb-argmin.png diff --git a/AM_statistics/Relation_argument_outer_token_distance.md b/AM_statistics/Relation_argument_outer_token_distance.md new file mode 100644 index 00000000..82e246bd --- /dev/null +++ b/AM_statistics/Relation_argument_outer_token_distance.md @@ -0,0 +1,156 @@ +# Relation argument (outer) token distance + +In this document, we collect statistics regarding the token distance between the argumentative units in a relation on our six argument-mining datasets. + +For the tokenization, we use `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased)) +to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). +The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance. + +We collect the following statistics: number of documents in the split (*no. doc*), no. of argumentative units (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). +We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. + +## AAE2 + +| statistics | train | test | +| :--------- | ------: | ------: | +| no. doc | 322 | 80 | +| len | 9002 | 2372 | +| mean | 102.582 | 100.711 | +| std | 93.76 | 92.698 | +| min | 9 | 10 | +| max | 514 | 442 | + +
+ Histogram (split: train, 322 documents) + +![rtd_aae2_train.png](img%2Frelation_token_distance%2Frtd_aae2_train.png) + +
+
+ Histogram (split: test, 80 documents) + +![rtd_aae2_test.png](img%2Frelation_token_distance%2Frtd_aae2_test.png) + +
+ +## AbsRCT + +Relation argument (outer) token distances (split: neoplasm_train, 350 documents) + +| statistics | neoplasm_train | neoplasm_dev | neoplasm_test | glaucoma_test | mixed_test | +| :--------- | -------------: | -----------: | ------------: | ------------: | ---------: | +| no. doc | 350 | 50 | 100 | 100 | 100 | +| len | 2836 | 438 | 848 | 734 | 658 | +| mean | 132.903 | 146.393 | 126.731 | 159.166 | 145.067 | +| std | 80.869 | 98.788 | 75.363 | 83.885 | 77.921 | +| min | 17 | 24 | 22 | 26 | 23 | +| max | 511 | 625 | 459 | 488 | 459 | + +
+ Histogram (split: neoplasm_train, 350 documents) + +![rtd_abs-neo_train.png](img%2Frelation_token_distance%2Frtd_abs-neo_train.png) + +
+
+ Histogram (split: neoplasm_dev, 50 documents) + +![rtd_abs-neo_dev.png](img%2Frelation_token_distance%2Frtd_abs-neo_dev.png) + +
+
+ Histogram (split: neoplasm_test, 100 documents) + +![rtd_abs-neo_test.png](img%2Frelation_token_distance%2Frtd_abs-neo_test.png) + +
+
+ Histogram (split: glucoma_test, 100 documents) + +![rtd_abs-glu_test.png](img%2Frelation_token_distance%2Frtd_abs-glu_test.png) + +
+
+ Histogram (split: mixed_test, 100 documents) + +![rtd_abs-mix_test.png](img%2Frelation_token_distance%2Frtd_abs-mix_test.png) + +
+ +## ArgMicro + +| statistics | train | +| :--------- | -----: | +| no. doc | 112 | +| len | 1018 | +| mean | 44.434 | +| std | 21.501 | +| min | 14 | +| max | 127 | + +
+ Histogram (split: train, 112 documents) + +![rtd_argmicro.png](img%2Frelation_token_distance%2Frtd_argmicro.png) + +
+ +## CDCP + +| statistics | train | test | +| :--------- | -----: | -----: | +| no. doc | 581 | 150 | +| len | 2204 | 648 | +| mean | 48.839 | 51.299 | +| std | 31.462 | 31.159 | +| min | 8 | 8 | +| max | 240 | 212 | + +
+ Histogram (split: train, 581 documents) + +![rtd_cdcp_train.png](img%2Frelation_token_distance%2Frtd_cdcp_train.png) + +
+
+ Histogram (split: test, 150 documents) + +![rtd_cdcp_test.png](img%2Frelation_token_distance%2Frtd_cdcp_test.png) + +
+ +## SciArg + +| statistics | train | +| :--------- | -----: | +| no. doc | 40 | +| len | 15640 | +| mean | 30.524 | +| std | 45.351 | +| min | 3 | +| max | 2864 | + +
+ Histogram (split: train, 40 documents) + +![rtd_sciarg.png](img%2Frelation_token_distance%2Frtd_sciarg.png) + +
+ +## SciDTB_Argmin + +| statistics | train | +| :--------- | -----: | +| no. doc | 60 | +| len | 586 | +| mean | 75.239 | +| std | 40.312 | +| min | 21 | +| max | 277 | + +
+ Histogram (split: train, 60 documents) + +![rtd_scidtb-argmin.png](img%2Frelation_token_distance%2Frtd_scidtb-argmin.png) + +
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We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. +### Usage + +To manually collect a statistics for each dataset, execute the command as follows. + +
+Command lines + +AAE2 + +``` +python src/evaluate_documents.py dataset=aae2_prepared metric=count_relation_argument_distances +``` + +AbsTRCT + +``` +python src/evaluate_documents.py dataset=abstrct_prepared metric=count_relation_argument_distances +``` + +ArgMicro + +``` +python src/evaluate_documents.py dataset=argmicro_prepared metric=count_relation_argument_distances +``` + +CDCP + +``` +python src/evaluate_documents.py dataset=cdcp_prepared metric=count_relation_argument_distances +``` + +SciArg + +``` +python src/evaluate_documents.py dataset=sciarg_prepared metric=count_relation_argument_distances ++metric.tokenize_kwargs.strict_span_conversion=false +``` + +SciDTB_Argmin + +``` +python src/evaluate_documents.py dataset=scidtb_argmin_prepared metric=count_relation_argument_distances +``` + +**Remark**: +The script `evaluate_documents.py` is from [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1). + +
+ ## AAE2 | statistics | train | test | @@ -20,13 +68,13 @@ We also present histograms in the collasible, showing the distribution of these | min | 9 | 10 | | max | 514 | 442 | -
+
Histogram (split: train, 322 documents) ![rtd_aae2_train.png](img%2Frelation_token_distance%2Frtd_aae2_train.png)
-
+
Histogram (split: test, 80 documents) ![rtd_aae2_test.png](img%2Frelation_token_distance%2Frtd_aae2_test.png) @@ -46,31 +94,31 @@ Relation argument (outer) token distances (split: neoplasm_train, 350 documents) | min | 17 | 24 | 22 | 26 | 23 | | max | 511 | 625 | 459 | 488 | 459 | -
+
Histogram (split: neoplasm_train, 350 documents) ![rtd_abs-neo_train.png](img%2Frelation_token_distance%2Frtd_abs-neo_train.png)
-
+
Histogram (split: neoplasm_dev, 50 documents) ![rtd_abs-neo_dev.png](img%2Frelation_token_distance%2Frtd_abs-neo_dev.png)
-
+
Histogram (split: neoplasm_test, 100 documents) ![rtd_abs-neo_test.png](img%2Frelation_token_distance%2Frtd_abs-neo_test.png)
-
+
Histogram (split: glucoma_test, 100 documents) ![rtd_abs-glu_test.png](img%2Frelation_token_distance%2Frtd_abs-glu_test.png)
-
+
Histogram (split: mixed_test, 100 documents) ![rtd_abs-mix_test.png](img%2Frelation_token_distance%2Frtd_abs-mix_test.png) @@ -88,7 +136,7 @@ Relation argument (outer) token distances (split: neoplasm_train, 350 documents) | min | 14 | | max | 127 | -
+
Histogram (split: train, 112 documents) ![rtd_argmicro.png](img%2Frelation_token_distance%2Frtd_argmicro.png) @@ -106,13 +154,13 @@ Relation argument (outer) token distances (split: neoplasm_train, 350 documents) | min | 8 | 8 | | max | 240 | 212 | -
+
Histogram (split: train, 581 documents) ![rtd_cdcp_train.png](img%2Frelation_token_distance%2Frtd_cdcp_train.png)
-
+
Histogram (split: test, 150 documents) ![rtd_cdcp_test.png](img%2Frelation_token_distance%2Frtd_cdcp_test.png) @@ -130,7 +178,7 @@ Relation argument (outer) token distances (split: neoplasm_train, 350 documents) | min | 3 | | max | 2864 | -
+
Histogram (split: train, 40 documents) ![rtd_sciarg.png](img%2Frelation_token_distance%2Frtd_sciarg.png) @@ -148,7 +196,7 @@ Relation argument (outer) token distances (split: neoplasm_train, 350 documents) | min | 21 | | max | 277 | -
+
Histogram (split: train, 60 documents) ![rtd_scidtb-argmin.png](img%2Frelation_token_distance%2Frtd_scidtb-argmin.png) From 0d266ed6ad037e4ab325288d2044181ea4b47058 Mon Sep 17 00:00:00 2001 From: Ruangrin L <88072261+idalr@users.noreply.github.com> Date: Sun, 28 Jan 2024 17:10:48 +0100 Subject: [PATCH 07/42] minor edits --- .../Relation_argument_outer_token_distance.md | 105 ++++++++++-------- 1 file changed, 58 insertions(+), 47 deletions(-) diff --git a/AM_statistics/Relation_argument_outer_token_distance.md b/AM_statistics/Relation_argument_outer_token_distance.md index 9fae1b04..9e4ba50e 100644 --- a/AM_statistics/Relation_argument_outer_token_distance.md +++ b/AM_statistics/Relation_argument_outer_token_distance.md @@ -9,53 +9,10 @@ The distance is measured from the first token of the first argumentative unit to We collect the following statistics: number of documents in the split (*no. doc*), no. of argumentative units (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. -### Usage - -To manually collect a statistics for each dataset, execute the command as follows. - -
-Command lines - -AAE2 - -``` -python src/evaluate_documents.py dataset=aae2_prepared metric=count_relation_argument_distances -``` - -AbsTRCT - -``` -python src/evaluate_documents.py dataset=abstrct_prepared metric=count_relation_argument_distances -``` - -ArgMicro - -``` -python src/evaluate_documents.py dataset=argmicro_prepared metric=count_relation_argument_distances -``` - -CDCP - -``` -python src/evaluate_documents.py dataset=cdcp_prepared metric=count_relation_argument_distances -``` - -SciArg - -``` -python src/evaluate_documents.py dataset=sciarg_prepared metric=count_relation_argument_distances ++metric.tokenize_kwargs.strict_span_conversion=false -``` - -SciDTB_Argmin - -``` -python src/evaluate_documents.py dataset=scidtb_argmin_prepared metric=count_relation_argument_distances -``` - -**Remark**: -The script `evaluate_documents.py` is from [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1). - -
+**Remark on statistics collection**: +To manually collect a statistics for each dataset, execute the command provided under each dataset, +using the base variant of the dataset configuration, namely, `*DATASET*_base`. +The script `evaluate_documents.py` comes from [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1). ## AAE2 @@ -81,6 +38,15 @@ The script `evaluate_documents.py` is from [PyTorch-IE-Hydra-Template](https://g
+
+Command + +``` +python src/evaluate_documents.py dataset=aae2_base metric=count_relation_argument_distances +``` + +
+ ## AbsRCT Relation argument (outer) token distances (split: neoplasm_train, 350 documents) @@ -125,6 +91,15 @@ Relation argument (outer) token distances (split: neoplasm_train, 350 documents)
+
+Command + +``` +python src/evaluate_documents.py dataset=abstrct_base metric=count_relation_argument_distances +``` + +
+ ## ArgMicro | statistics | train | @@ -143,6 +118,15 @@ Relation argument (outer) token distances (split: neoplasm_train, 350 documents)
+
+Command + +``` +python src/evaluate_documents.py dataset=argmicro_base metric=count_relation_argument_distances +``` + +
+ ## CDCP | statistics | train | test | @@ -167,6 +151,15 @@ Relation argument (outer) token distances (split: neoplasm_train, 350 documents)
+
+Command + +``` +python src/evaluate_documents.py dataset=cdcp_base metric=count_relation_argument_distances +``` + +
+ ## SciArg | statistics | train | @@ -185,6 +178,15 @@ Relation argument (outer) token distances (split: neoplasm_train, 350 documents)
+
+Command + +``` +python src/evaluate_documents.py dataset=sciarg_base metric=count_relation_argument_distances ++metric.tokenize_kwargs.strict_span_conversion=false +``` + +
+ ## SciDTB_Argmin | statistics | train | @@ -202,3 +204,12 @@ Relation argument (outer) token distances (split: neoplasm_train, 350 documents) ![rtd_scidtb-argmin.png](img%2Frelation_token_distance%2Frtd_scidtb-argmin.png)
+ +
+Command + +``` +python src/evaluate_documents.py dataset=scidtb_argmin_base metric=count_relation_argument_distances +``` + +
From 839a8011f8d1713782631f5f739ad8b186a46a10 Mon Sep 17 00:00:00 2001 From: Ruangrin L <88072261+idalr@users.noreply.github.com> Date: Wed, 31 Jan 2024 12:51:27 +0100 Subject: [PATCH 08/42] added text_length_tokens.md and minor edit --- .../Relation_argument_outer_token_distance.md | 2 - AM_statistics/img/token_len/tl_aae2_test.png | Bin 0 -> 11504 bytes AM_statistics/img/token_len/tl_aae2_train.png | Bin 0 -> 12821 bytes .../img/token_len/tl_abs-glu_test.png | Bin 0 -> 11715 bytes .../img/token_len/tl_abs-mix_test.png | Bin 0 -> 12049 bytes .../img/token_len/tl_abs-neo_dev.png | Bin 0 -> 11669 bytes .../img/token_len/tl_abs-neo_test.png | Bin 0 -> 12109 bytes .../img/token_len/tl_abs-neo_train.png | Bin 0 -> 12287 bytes AM_statistics/img/token_len/tl_argmicro.png | Bin 0 -> 10254 bytes AM_statistics/img/token_len/tl_cdcp_test.png | Bin 0 -> 12278 bytes AM_statistics/img/token_len/tl_cdcp_train.png | Bin 0 -> 13037 bytes AM_statistics/img/token_len/tl_sciarg.png | Bin 0 -> 11574 bytes .../img/token_len/tl_scidtb-argmin.png | Bin 0 -> 12287 bytes AM_statistics/text_length_tokens.md | 207 ++++++++++++++++++ 14 files changed, 207 insertions(+), 2 deletions(-) create mode 100644 AM_statistics/img/token_len/tl_aae2_test.png create mode 100644 AM_statistics/img/token_len/tl_aae2_train.png create mode 100644 AM_statistics/img/token_len/tl_abs-glu_test.png create mode 100644 AM_statistics/img/token_len/tl_abs-mix_test.png create mode 100644 AM_statistics/img/token_len/tl_abs-neo_dev.png create mode 100644 AM_statistics/img/token_len/tl_abs-neo_test.png create mode 100644 AM_statistics/img/token_len/tl_abs-neo_train.png create mode 100644 AM_statistics/img/token_len/tl_argmicro.png create mode 100644 AM_statistics/img/token_len/tl_cdcp_test.png create mode 100644 AM_statistics/img/token_len/tl_cdcp_train.png create mode 100644 AM_statistics/img/token_len/tl_sciarg.png create mode 100644 AM_statistics/img/token_len/tl_scidtb-argmin.png create mode 100644 AM_statistics/text_length_tokens.md diff --git a/AM_statistics/Relation_argument_outer_token_distance.md b/AM_statistics/Relation_argument_outer_token_distance.md index 9e4ba50e..384fc567 100644 --- a/AM_statistics/Relation_argument_outer_token_distance.md +++ b/AM_statistics/Relation_argument_outer_token_distance.md @@ -49,8 +49,6 @@ python src/evaluate_documents.py dataset=aae2_base metric=count_relation_argumen ## AbsRCT -Relation argument (outer) token distances (split: neoplasm_train, 350 documents) - | statistics | neoplasm_train | neoplasm_dev | neoplasm_test | glaucoma_test | mixed_test | | :--------- | -------------: | -----------: | ------------: | ------------: | ---------: | | no. doc | 350 | 50 | 100 | 100 | 100 | diff --git a/AM_statistics/img/token_len/tl_aae2_test.png b/AM_statistics/img/token_len/tl_aae2_test.png new file mode 100644 index 0000000000000000000000000000000000000000..d3f8cf5c590be00271776bee089deffac830c7be GIT binary patch literal 11504 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Histogram (split: train, 322 documents) + +![tl_aae2_train.png](img%2Ftoken_len%2Ftl_aae2_train.png) + +
+
+ Histogram (split: test, 80 documents) + +![tl_aae2_test.png](img%2Ftoken_len%2Ftl_aae2_test.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=aae2_base metric=count_text_tokens +``` + +
+ +## AbsRCT + +| statistics | neoplasm_train | neoplasm_dev | neoplasm_test | glaucoma_test | mixed_test | +| :--------- | -------------: | -----------: | ------------: | ------------: | ---------: | +| no. doc | 350 | 50 | 100 | 100 | 100 | +| mean | 447.291 | 481.66 | 442.79 | 456.78 | 450.29 | +| std | 91.266 | 116.239 | 89.692 | 115.535 | 87.002 | +| min | 301 | 329 | 292 | 212 | 268 | +| max | 843 | 952 | 776 | 1022 | 776 | + +
+ Histogram (split: neoplasm_train, 350 documents) + +![tl_abs-neo_train.png](img%2Ftoken_len%2Ftl_abs-neo_train.png) + +
+
+ Histogram (split: neoplasm_dev, 50 documents) + +![tl_abs-neo_dev.png](img%2Ftoken_len%2Ftl_abs-neo_dev.png) + +
+
+ Histogram (split: neoplasm_test, 100 documents) + +![tl_abs-neo_test.png](img%2Ftoken_len%2Ftl_abs-neo_test.png) + +
+
+ Histogram (split: glucoma_test, 100 documents) + +![tl_abs-glu_test.png](img%2Ftoken_len%2Ftl_abs-glu_test.png) + +
+
+ Histogram (split: mixed_test, 100 documents) + +![tl_abs-mix_test.png](img%2Ftoken_len%2Ftl_abs-mix_test.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=abstrct_base metric=count_text_tokens +``` + +
+ +## ArgMicro + +| statistics | train | +| :--------- | -----: | +| no. doc | 112 | +| mean | 84.161 | +| std | 22.596 | +| min | 36 | +| max | 153 | + +
+ Histogram (split: train, 112 documents) + +![tl_argmicro.png](img%2Ftoken_len%2Ftl_argmicro.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=argmicro_base metric=count_text_tokens +``` + +
+ +## CDCP + +| statistics | train | test | +| :--------- | ------: | ------: | +| no. doc | 581 | 150 | +| mean | 130.781 | 128.673 | +| std | 101.121 | 98.708 | +| min | 13 | 15 | +| max | 562 | 571 | + +
+ Histogram (split: train, 580 documents) + +![tl_cdcp_train.png](img%2Ftoken_len%2Ftl_cdcp_train.png) + +
+
+ Histogram (split: test, 150 documents) + +![tl_cdcp_test.png](img%2Ftoken_len%2Ftl_cdcp_test.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=cdcp_base metric=count_text_tokens +``` + +
+ +## SciArg + +| statistics | train | +| :--------- | ------: | +| no. doc | 40 | +| mean | 10521.1 | +| std | 2472.2 | +| min | 6452 | +| max | 16421 | + +
+ Histogram (split: train, 40 documents) + +![tl_sciarg.png](img%2Ftoken_len%2Ftl_sciarg.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=sciarg_base metric=count_text_tokens +``` + +
+ +## SciDTB_Argmin + +| statistics | train | +| :--------- | ------: | +| no. doc | 60 | +| mean | 164.417 | +| std | 64.572 | +| min | 80 | +| max | 532 | + +
+ Histogram (split: train, 60 documents) + +![tl_scidtb-argmin.png](img%2Ftoken_len%2Ftl_scidtb-argmin.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=scidtb_argmin_base metric=count_text_tokens +``` + +
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-> 13167 bytes AM_statistics/img/span_len_token/slt_sciarg.png | Bin 0 -> 13302 bytes .../img/span_len_token/slt_scitdb-argmin.png | Bin 0 -> 12514 bytes 12 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 AM_statistics/img/span_len_token/slt_aae2_test.png create mode 100644 AM_statistics/img/span_len_token/slt_aae2_train.png create mode 100644 AM_statistics/img/span_len_token/slt_abs-glu_test.png create mode 100644 AM_statistics/img/span_len_token/slt_abs-mix_test.png create mode 100644 AM_statistics/img/span_len_token/slt_abs-neo_dev.png create mode 100644 AM_statistics/img/span_len_token/slt_abs-neo_test.png create mode 100644 AM_statistics/img/span_len_token/slt_abs-neo_train.png create mode 100644 AM_statistics/img/span_len_token/slt_argmicro.png create mode 100644 AM_statistics/img/span_len_token/slt_cdcp_test.png create mode 100644 AM_statistics/img/span_len_token/slt_cdcp_train.png create mode 100644 AM_statistics/img/span_len_token/slt_sciarg.png create mode 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++++++++++++++++++ AM_statistics/text_length_tokens.md | 6 +- 3 files changed, 219 insertions(+), 6 deletions(-) create mode 100644 AM_statistics/span_lengths_tokens.md diff --git a/AM_statistics/Relation_argument_outer_token_distance.md b/AM_statistics/Relation_argument_outer_token_distance.md index 384fc567..2e531377 100644 --- a/AM_statistics/Relation_argument_outer_token_distance.md +++ b/AM_statistics/Relation_argument_outer_token_distance.md @@ -6,7 +6,7 @@ For the tokenization, we use `bert-base-uncased` from `transformer.AutoTokenizer to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance. -We collect the following statistics: number of documents in the split (*no. doc*), no. of argumentative units (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). +We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. **Remark on statistics collection**: @@ -129,7 +129,7 @@ python src/evaluate_documents.py dataset=argmicro_base metric=count_relation_arg | statistics | train | test | | :--------- | -----: | -----: | -| no. doc | 581 | 150 | +| no. doc | 580 | 150 | | len | 2204 | 648 | | mean | 48.839 | 51.299 | | std | 31.462 | 31.159 | @@ -137,7 +137,7 @@ python src/evaluate_documents.py dataset=argmicro_base metric=count_relation_arg | max | 240 | 212 |

- Histogram (split: train, 581 documents) + Histogram (split: train, 580 documents) ![rtd_cdcp_train.png](img%2Frelation_token_distance%2Frtd_cdcp_train.png) diff --git a/AM_statistics/span_lengths_tokens.md b/AM_statistics/span_lengths_tokens.md new file mode 100644 index 00000000..0a1960b0 --- /dev/null +++ b/AM_statistics/span_lengths_tokens.md @@ -0,0 +1,213 @@ +# Span lengths (tokens) + +In this document, we collect statistics regarding the token distance between the argumentative units in a relation on our six argument-mining datasets. + +For the tokenization, we use `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased)) +to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). +The span length is measured from the first token of the first argumentative unit to the last token of the particular unit. + +We collect the following statistics: number of documents in the split (*no. doc*), no. of spans (*len*), mean of number of tokens in a span (*mean*), standard deviation of the number of tokens (*std*), minimum tokens in a span (*min*), and maximum tokens in a span (*max*). +We also present histograms in the collasible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly. + +**Remark on statistics collection**: +To manually collect a statistics for each dataset, execute the command provided under each dataset, +using the base variant of the dataset configuration, namely, `*DATASET*_base`. +The script `evaluate_documents.py` comes from [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1). + +## AAE2 + +| statistics | train | test | +| :--------- | -----: | -----: | +| no. doc | 322 | 80 | +| len | 4823 | 1266 | +| mean | 17.157 | 16.317 | +| std | 8.079 | 7.953 | +| min | 3 | 3 | +| max | 75 | 50 | + +
+ Histogram (split: train, 322 documents) + +![slt_aae2_train.png](img%2Fspan_len_token%2Fslt_aae2_train.png) + +
+
+ Histogram (split: test, 80 documents) + +![slt_aae2_test.png](img%2Fspan_len_token%2Fslt_aae2_test.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=aae2_base metric=span_lengths_tokens +``` + +
+ +## AbsRCT + +| statistics | neoplasm_train | neoplasm_dev | neoplasm_test | glaucoma_test | mixed_test | +| :--------- | -------------: | -----------: | ------------: | ------------: | ---------: | +| no. doc | 350 | 50 | 100 | 100 | 100 | +| len | 2267 | 326 | 686 | 594 | 600 | +| mean | 34.303 | 37.135 | 32.566 | 38.997 | 38.507 | +| std | 22.425 | 29.941 | 20.264 | 22.604 | 24.036 | +| min | 5 | 5 | 6 | 6 | 7 | +| max | 250 | 288 | 182 | 169 | 159 | + +
+ Histogram (split: neoplasm_train, 350 documents) + +![slt_abs-neo_train.png](img%2Fspan_len_token%2Fslt_abs-neo_train.png) + +
+
+ Histogram (split: neoplasm_dev, 50 documents) + +![slt_abs-neo_dev.png](img%2Fspan_len_token%2Fslt_abs-neo_dev.png) + +
+
+ Histogram (split: neoplasm_test, 100 documents) + +![slt_abs-neo_test.png](img%2Fspan_len_token%2Fslt_abs-neo_test.png) + +
+
+ Histogram (split: glucoma_test, 100 documents) + +![slt_abs-glu_test.png](img%2Fspan_len_token%2Fslt_abs-glu_test.png) + +
+
+ Histogram (split: mixed_test, 100 documents) + +![slt_abs-mix_test.png](img%2Fspan_len_token%2Fslt_abs-mix_test.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=abstrct_base metric=span_lengths_tokens +``` + +
+ +## ArgMicro + +| statistics | train | +| :--------- | -----: | +| no. doc | 112 | +| len | 576 | +| mean | 16.365 | +| std | 6.545 | +| min | 4 | +| max | 41 | + +
+ Histogram (split: train, 112 documents) + +![slt_argmicro.png](img%2Fspan_len_token%2Fslt_argmicro.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=argmicro_base metric=span_lengths_tokens +``` + +
+ +## CDCP + +| statistics | train | test | +| :--------- | -----: | -----: | +| no. doc | 580 | 150 | +| len | 3901 | 1026 | +| mean | 19.441 | 18.758 | +| std | 11.71 | 10.388 | +| min | 2 | 3 | +| max | 142 | 83 | + +
+ Histogram (split: train, 580 documents) + +![slt_cdcp_train.png](img%2Fspan_len_token%2Fslt_cdcp_train.png) + +
+
+ Histogram (split: test, 150 documents) + +![slt_cdcp_test.png](img%2Fspan_len_token%2Fslt_cdcp_test.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=cdcp_base metric=span_lengths_tokens +``` + +
+ +## SciArg + +| statistics | train | +| :--------- | -----: | +| no. doc | 40 | +| len | 13586 | +| mean | 11.677 | +| std | 8.731 | +| min | 1 | +| max | 138 | + +
+ Histogram (split: train, 40 documents) + +![slt_sciarg.png](img%2Fspan_len_token%2Fslt_sciarg.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=sciarg_base metric=span_lengths_tokens ++metric.tokenize_kwargs.strict_span_conversion=false +``` + +
+ +## SciDTB_Argmin + +| statistics | train | +| :--------- | -----: | +| no. doc | 60 | +| len | 353 | +| mean | 27.946 | +| std | 13.054 | +| min | 7 | +| max | 123 | + +
+ Histogram (split: train, 60 documents) + +![slt_scitdb-argmin.png](img%2Fspan_len_token%2Fslt_scitdb-argmin.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=scidtb_argmin_base metric=span_lengths_tokens +``` + +
diff --git a/AM_statistics/text_length_tokens.md b/AM_statistics/text_length_tokens.md index 7139e5e6..c951fb3b 100644 --- a/AM_statistics/text_length_tokens.md +++ b/AM_statistics/text_length_tokens.md @@ -4,10 +4,10 @@ In this document, we collect statistics regarding the token distance between the For the tokenization, we use `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased)) to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). -The length is measured from the first token of the document to the last one. +The text (document) length is measured from the first token of the document to the last one. We collect the following statistics: number of documents in the split (*no. doc*), mean of document token-length (*mean*), standard deviation of the length (*std*), minimum number of tokens in a document (*min*), and maximum number of tokens in a document (*max*). -We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collasible, showing the distribution of these text lengths (x-axis; and unit-counts in y-axis), accordingly. **Remark on statistics collection**: To manually collect a statistics for each dataset, execute the command provided under each dataset, @@ -126,7 +126,7 @@ python src/evaluate_documents.py dataset=argmicro_base metric=count_text_tokens | statistics | train | test | | :--------- | ------: | ------: | -| no. doc | 581 | 150 | +| no. doc | 580 | 150 | | mean | 130.781 | 128.673 | | std | 101.121 | 98.708 | | min | 13 | 15 | From 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relation_argument_outer_token_distance_per_label.md --- ...argument_outer_token_distance_per_label.md | 269 ++++++++++++++++++ .../rtd-label_aae2_test.png | Bin 0 -> 14400 bytes .../rtd-label_aae2_train.png | Bin 0 -> 14400 bytes .../rtd-label_abs-glu_test.png | Bin 0 -> 17093 bytes .../rtd-label_abs-mix_test.png | Bin 0 -> 15611 bytes .../rtd-label_abs-neo_dev.png | Bin 0 -> 14896 bytes .../rtd-label_abs-neo_test.png | Bin 0 -> 17181 bytes .../rtd-label_abs-neo_train.png | Bin 0 -> 17315 bytes .../rtd-label_argmicro.png | Bin 0 -> 16120 bytes .../rtd-label_cdcp_test.png | Bin 0 -> 15391 bytes .../rtd-label_cdcp_train.png | Bin 0 -> 16507 bytes .../rtd-label_sciarg.png | Bin 0 -> 17272 bytes .../rtd-label_scitdb-argmin.png | Bin 0 -> 16871 bytes 13 files changed, 269 insertions(+) create mode 100644 AM_statistics/Relation_argument_outer_token_distance_per_label.md create mode 100644 AM_statistics/img/relation_token_distance_per_label/rtd-label_aae2_test.png create mode 100644 AM_statistics/img/relation_token_distance_per_label/rtd-label_aae2_train.png create mode 100644 AM_statistics/img/relation_token_distance_per_label/rtd-label_abs-glu_test.png create mode 100644 AM_statistics/img/relation_token_distance_per_label/rtd-label_abs-mix_test.png create mode 100644 AM_statistics/img/relation_token_distance_per_label/rtd-label_abs-neo_dev.png create mode 100644 AM_statistics/img/relation_token_distance_per_label/rtd-label_abs-neo_test.png create mode 100644 AM_statistics/img/relation_token_distance_per_label/rtd-label_abs-neo_train.png create mode 100644 AM_statistics/img/relation_token_distance_per_label/rtd-label_argmicro.png create mode 100644 AM_statistics/img/relation_token_distance_per_label/rtd-label_cdcp_test.png create mode 100644 AM_statistics/img/relation_token_distance_per_label/rtd-label_cdcp_train.png create mode 100644 AM_statistics/img/relation_token_distance_per_label/rtd-label_sciarg.png create mode 100644 AM_statistics/img/relation_token_distance_per_label/rtd-label_scitdb-argmin.png diff --git a/AM_statistics/Relation_argument_outer_token_distance_per_label.md b/AM_statistics/Relation_argument_outer_token_distance_per_label.md new file mode 100644 index 00000000..4376ac17 --- /dev/null +++ b/AM_statistics/Relation_argument_outer_token_distance_per_label.md @@ -0,0 +1,269 @@ +# Relation argument (outer) token distance per label + +In this document, we collect statistics regarding the token distance between the argumentative units in a relation on our six argument-mining datasets. + +For the tokenization, we use `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased)) +to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). +The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance. + +We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). +We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. + +**Remark on statistics collection**: +To manually collect a statistics for each dataset, execute the command provided under each dataset, +using the base variant of the dataset configuration, namely, `*DATASET*_base`. +The script `evaluate_documents.py` comes from [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1). + +## AAE2 + +### train (322 documents) + +| | len | max | mean | min | std | +| :---------------- | ---: | --: | ------: | --: | ------: | +| ALL | 9002 | 514 | 102.582 | 9 | 93.76 | +| attacks | 810 | 442 | 127.622 | 10 | 109.283 | +| semantically_same | 552 | 514 | 301.638 | 25 | 73.756 | +| supports | 7640 | 493 | 85.545 | 9 | 74.023 | + +
+ Histogram (split: train, 322 documents) + +![img_1.png](img/relation_token_distance_per_label/rtd-label_aae2_train.png) + +
+ +### test (80 documents) + +| | len | max | mean | min | std | +| :---------------- | ---: | --: | ------: | --: | -----: | +| ALL | 2372 | 442 | 100.711 | 10 | 92.698 | +| attacks | 184 | 402 | 115.891 | 12 | 98.751 | +| semantically_same | 146 | 442 | 299.671 | 34 | 72.921 | +| supports | 2042 | 437 | 85.118 | 10 | 75.023 | + +
+ Histogram (split: test, 80 documents) + +![img.png](img/relation_token_distance_per_label/rtd-label_aae2_test.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=aae2_base metric=count_relation_argument_distances +``` + +
+ +## AbsRCT + +### neoplasm_train (350 documents) + +| | len | max | mean | min | std | +| :------------- | ---: | --: | ------: | --: | -----: | +| ALL | 2836 | 511 | 132.903 | 17 | 80.869 | +| Attack | 72 | 346 | 89.639 | 29 | 75.554 | +| Partial-Attack | 338 | 324 | 59.024 | 17 | 42.773 | +| Support | 2426 | 511 | 144.481 | 26 | 79.187 | + +
+ Histogram (split: neoplasm_train, 350 documents) + +![img_2.png](img/relation_token_distance_per_label/rtd-label_abs-neo_train.png) + +
+ +### neoplasm_dev (50 documents) + +| | len | max | mean | min | std | +| :------------- | --: | --: | ------: | --: | -----: | +| ALL | 438 | 625 | 146.393 | 24 | 98.788 | +| Attack | 16 | 200 | 90.375 | 26 | 62.628 | +| Partial-Attack | 50 | 240 | 72.04 | 24 | 47.685 | +| Support | 372 | 625 | 158.796 | 34 | 99.922 | + +
+ Histogram (split: neoplasm_dev, 50 documents) + +![img_3.png](img/relation_token_distance_per_label/rtd-label_abs-neo_dev.png) + +
+ +### neoplasm_test (100 documents) + +| | len | max | mean | min | std | +| :------------- | --: | --: | ------: | --: | -----: | +| ALL | 848 | 459 | 126.731 | 22 | 75.363 | +| Attack | 32 | 390 | 115.688 | 22 | 97.262 | +| Partial-Attack | 88 | 205 | 56.955 | 24 | 34.534 | +| Support | 728 | 459 | 135.651 | 33 | 73.365 | + +
+ Histogram (split: neoplasm_test, 100 documents) + +![img_4.png](img/relation_token_distance_per_label/rtd-label_abs-neo_test.png) + +
+ +### glaucoma_test (100 documents) + +| | len | max | mean | min | std | +| :------------- | --: | --: | ------: | --: | -----: | +| ALL | 734 | 488 | 159.166 | 26 | 83.885 | +| Attack | 14 | 177 | 89 | 47 | 40.171 | +| Partial-Attack | 52 | 259 | 74 | 26 | 51.239 | +| Support | 668 | 488 | 167.266 | 38 | 82.222 | + +
+ Histogram (split: glaucoma_test, 100 documents) + +![img_5.png](img/relation_token_distance_per_label/rtd-label_abs-glu_test.png) + +
+ +### mixed_test (100 documents) + +| | len | max | mean | min | std | +| :------------- | --: | --: | ------: | --: | ------: | +| ALL | 658 | 459 | 145.067 | 23 | 77.921 | +| Attack | 6 | 411 | 164 | 34 | 174.736 | +| Partial-Attack | 42 | 259 | 65.762 | 23 | 62.426 | +| Support | 610 | 459 | 150.341 | 35 | 74.273 | + +
+ Histogram (split: mixed_test, 100 documents) + +![img_6.png](img/relation_token_distance_per_label/rtd-label_abs-mix_test.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=abstrct_base metric=count_relation_argument_distances +``` + +
+ +## ArgMicro + +| | len | max | mean | min | std | +| :---- | ---: | --: | -----: | --: | -----: | +| ALL | 1018 | 127 | 44.434 | 14 | 21.501 | +| exa | 18 | 63 | 33.556 | 16 | 13.056 | +| joint | 88 | 48 | 30.091 | 17 | 9.075 | +| reb | 220 | 127 | 49.327 | 16 | 24.653 | +| sup | 562 | 124 | 46.534 | 14 | 22.079 | +| und | 130 | 84 | 38.292 | 17 | 12.321 | + +
+ Histogram (split: train, 112 documents) + +![img_7.png](img/relation_token_distance_per_label/rtd-label_argmicro.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=argmicro_base metric=count_relation_argument_distances +``` + +
+ +## CDCP + +### train (580 documents) + +| | len | max | mean | min | std | +| :------- | ---: | --: | -----: | --: | -----: | +| ALL | 2204 | 240 | 48.839 | 8 | 31.462 | +| evidence | 94 | 196 | 66.723 | 14 | 42.444 | +| reason | 2110 | 240 | 48.043 | 8 | 30.64 | + +
+ Histogram (split: train, 580 documents) + +![img_8.png](img/relation_token_distance_per_label/rtd-label_cdcp_train.png) + +
+ +### test (150 documents) + +| | len | max | mean | min | std | +| :------- | --: | --: | -----: | --: | -----: | +| ALL | 648 | 212 | 51.299 | 8 | 31.159 | +| evidence | 52 | 170 | 73.923 | 20 | 39.855 | +| reason | 596 | 212 | 49.326 | 8 | 29.47 | + +
+ Histogram (split: test, 150 documents) + +![img_9.png](img/relation_token_distance_per_label/rtd-label_cdcp_test.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=cdcp_base metric=count_relation_argument_distances +``` + +
+ +## SciArg + +| | len | max | mean | min | std | +| :---------------- | ----: | ---: | ------: | --: | ------: | +| ALL | 15640 | 2864 | 30.524 | 3 | 45.351 | +| contradicts | 1392 | 238 | 32.565 | 6 | 19.771 | +| parts_of_same | 2594 | 374 | 28.18 | 3 | 26.845 | +| semantically_same | 84 | 2864 | 206.333 | 11 | 492.268 | +| supports | 11570 | 407 | 29.527 | 4 | 24.189 | + +
+ Histogram (split: train, 40 documents) + +![img_10.png](img/relation_token_distance_per_label/rtd-label_sciarg.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=sciarg_base metric=count_relation_argument_distances ++metric.tokenize_kwargs.strict_span_conversion=false +``` + +
+ +## SciDTB_Argmin + +| | len | max | mean | min | std | +| :--------- | --: | --: | -----: | --: | -----: | +| ALL | 586 | 277 | 75.239 | 21 | 40.312 | +| additional | 54 | 180 | 59.593 | 36 | 29.306 | +| detail | 258 | 163 | 65.62 | 22 | 29.21 | +| sequence | 22 | 93 | 59.727 | 38 | 17.205 | +| support | 252 | 277 | 89.794 | 21 | 48.118 | + +
+ Histogram (split: train, 60 documents) + +![img_11.png](img/relation_token_distance_per_label/rtd-label_scitdb-argmin.png) + +
+ +
+Command + +``` +python src/evaluate_documents.py dataset=scidtb_argmin_base metric=count_relation_argument_distances +``` + +
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.../rtd_abs-neo_test.png | Bin 12849 -> 0 bytes .../rtd_abs-neo_train.png | Bin 13259 -> 0 bytes .../relation_token_distance/rtd_argmicro.png | Bin 12306 -> 0 bytes .../relation_token_distance/rtd_cdcp_test.png | Bin 11771 -> 0 bytes .../rtd_cdcp_train.png | Bin 13228 -> 0 bytes .../relation_token_distance/rtd_sciarg.png | Bin 13271 -> 0 bytes .../rtd_scidtb-argmin.png | Bin 12286 -> 0 bytes 13 files changed, 213 deletions(-) delete mode 100644 AM_statistics/Relation_argument_outer_token_distance.md delete mode 100644 AM_statistics/img/relation_token_distance/rtd_aae2_test.png delete mode 100644 AM_statistics/img/relation_token_distance/rtd_aae2_train.png delete mode 100644 AM_statistics/img/relation_token_distance/rtd_abs-glu_test.png delete mode 100644 AM_statistics/img/relation_token_distance/rtd_abs-mix_test.png delete mode 100644 AM_statistics/img/relation_token_distance/rtd_abs-neo_dev.png delete mode 100644 AM_statistics/img/relation_token_distance/rtd_abs-neo_test.png delete mode 100644 AM_statistics/img/relation_token_distance/rtd_abs-neo_train.png delete mode 100644 AM_statistics/img/relation_token_distance/rtd_argmicro.png delete mode 100644 AM_statistics/img/relation_token_distance/rtd_cdcp_test.png delete mode 100644 AM_statistics/img/relation_token_distance/rtd_cdcp_train.png delete mode 100644 AM_statistics/img/relation_token_distance/rtd_sciarg.png delete mode 100644 AM_statistics/img/relation_token_distance/rtd_scidtb-argmin.png diff --git a/AM_statistics/Relation_argument_outer_token_distance.md b/AM_statistics/Relation_argument_outer_token_distance.md deleted file mode 100644 index 2e531377..00000000 --- a/AM_statistics/Relation_argument_outer_token_distance.md +++ /dev/null @@ -1,213 +0,0 @@ -# Relation argument (outer) token distance - -In this document, we collect statistics regarding the token distance between the argumentative units in a relation on our six argument-mining datasets. - -For the tokenization, we use `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased)) -to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). -The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance. - -We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). -We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. - -**Remark on statistics collection**: -To manually collect a statistics for each dataset, execute the command provided under each dataset, -using the base variant of the dataset configuration, namely, `*DATASET*_base`. -The script `evaluate_documents.py` comes from [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1). - -## AAE2 - -| statistics | train | test | -| :--------- | ------: | ------: | -| no. doc | 322 | 80 | -| len | 9002 | 2372 | -| mean | 102.582 | 100.711 | -| std | 93.76 | 92.698 | -| min | 9 | 10 | -| max | 514 | 442 | - -
- Histogram (split: train, 322 documents) - -![rtd_aae2_train.png](img%2Frelation_token_distance%2Frtd_aae2_train.png) - -
-
- Histogram (split: test, 80 documents) - -![rtd_aae2_test.png](img%2Frelation_token_distance%2Frtd_aae2_test.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=aae2_base metric=count_relation_argument_distances -``` - -
- -## AbsRCT - -| statistics | neoplasm_train | neoplasm_dev | neoplasm_test | glaucoma_test | mixed_test | -| :--------- | -------------: | -----------: | ------------: | ------------: | ---------: | -| no. doc | 350 | 50 | 100 | 100 | 100 | -| len | 2836 | 438 | 848 | 734 | 658 | -| mean | 132.903 | 146.393 | 126.731 | 159.166 | 145.067 | -| std | 80.869 | 98.788 | 75.363 | 83.885 | 77.921 | -| min | 17 | 24 | 22 | 26 | 23 | -| max | 511 | 625 | 459 | 488 | 459 | - -
- Histogram (split: neoplasm_train, 350 documents) - -![rtd_abs-neo_train.png](img%2Frelation_token_distance%2Frtd_abs-neo_train.png) - -
-
- Histogram (split: neoplasm_dev, 50 documents) - -![rtd_abs-neo_dev.png](img%2Frelation_token_distance%2Frtd_abs-neo_dev.png) - -
-
- Histogram (split: neoplasm_test, 100 documents) - -![rtd_abs-neo_test.png](img%2Frelation_token_distance%2Frtd_abs-neo_test.png) - -
-
- Histogram (split: glucoma_test, 100 documents) - -![rtd_abs-glu_test.png](img%2Frelation_token_distance%2Frtd_abs-glu_test.png) - -
-
- Histogram (split: mixed_test, 100 documents) - -![rtd_abs-mix_test.png](img%2Frelation_token_distance%2Frtd_abs-mix_test.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=abstrct_base metric=count_relation_argument_distances -``` - -
- -## ArgMicro - -| statistics | train | -| :--------- | -----: | -| no. doc | 112 | -| len | 1018 | -| mean | 44.434 | -| std | 21.501 | -| min | 14 | -| max | 127 | - -
- Histogram (split: train, 112 documents) - -![rtd_argmicro.png](img%2Frelation_token_distance%2Frtd_argmicro.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=argmicro_base metric=count_relation_argument_distances -``` - -
- -## CDCP - -| statistics | train | test | -| :--------- | -----: | -----: | -| no. doc | 580 | 150 | -| len | 2204 | 648 | -| mean | 48.839 | 51.299 | -| std | 31.462 | 31.159 | -| min | 8 | 8 | -| max | 240 | 212 | - -
- Histogram (split: train, 580 documents) - -![rtd_cdcp_train.png](img%2Frelation_token_distance%2Frtd_cdcp_train.png) - -
-
- Histogram (split: test, 150 documents) - -![rtd_cdcp_test.png](img%2Frelation_token_distance%2Frtd_cdcp_test.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=cdcp_base metric=count_relation_argument_distances -``` - -
- -## SciArg - -| statistics | train | -| :--------- | -----: | -| no. doc | 40 | -| len | 15640 | -| mean | 30.524 | -| std | 45.351 | -| min | 3 | -| max | 2864 | - -
- Histogram (split: train, 40 documents) - -![rtd_sciarg.png](img%2Frelation_token_distance%2Frtd_sciarg.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=sciarg_base metric=count_relation_argument_distances ++metric.tokenize_kwargs.strict_span_conversion=false -``` - -
- -## SciDTB_Argmin - -| statistics | train | -| :--------- | -----: | -| no. doc | 60 | -| len | 586 | -| mean | 75.239 | -| std | 40.312 | -| min | 21 | -| max | 277 | - -
- Histogram (split: train, 60 documents) - -![rtd_scidtb-argmin.png](img%2Frelation_token_distance%2Frtd_scidtb-argmin.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=scidtb_argmin_base metric=count_relation_argument_distances -``` - -
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+ +##### neoplasm_train (350 documents) + +| | len | max | mean | min | std | +| :------------- | ---: | --: | ------: | --: | -----: | +| ALL | 2836 | 511 | 132.903 | 17 | 80.869 | +| Attack | 72 | 346 | 89.639 | 29 | 75.554 | +| Partial-Attack | 338 | 324 | 59.024 | 17 | 42.773 | +| Support | 2426 | 511 | 144.481 | 26 | 79.187 | + +
+ Histogram (split: neoplasm_train, 350 documents) + +![img_2.png](img/rtd-label_abs-neo_train.png) + +
+ +##### neoplasm_dev (50 documents) + +| | len | max | mean | min | std | +| :------------- | --: | --: | ------: | --: | -----: | +| ALL | 438 | 625 | 146.393 | 24 | 98.788 | +| Attack | 16 | 200 | 90.375 | 26 | 62.628 | +| Partial-Attack | 50 | 240 | 72.04 | 24 | 47.685 | +| Support | 372 | 625 | 158.796 | 34 | 99.922 | + +
+ Histogram (split: neoplasm_dev, 50 documents) + +![img_3.png](img/rtd-label_abs-neo_dev.png) + +
+ +##### neoplasm_test (100 documents) + +| | len | max | mean | min | std | +| :------------- | --: | --: | ------: | --: | -----: | +| ALL | 848 | 459 | 126.731 | 22 | 75.363 | +| Attack | 32 | 390 | 115.688 | 22 | 97.262 | +| Partial-Attack | 88 | 205 | 56.955 | 24 | 34.534 | +| Support | 728 | 459 | 135.651 | 33 | 73.365 | + +
+ Histogram (split: neoplasm_test, 100 documents) + +![img_4.png](img/rtd-label_abs-neo_test.png) + +
+ +##### glaucoma_test (100 documents) + +| | len | max | mean | min | std | +| :------------- | --: | --: | ------: | --: | -----: | +| ALL | 734 | 488 | 159.166 | 26 | 83.885 | +| Attack | 14 | 177 | 89 | 47 | 40.171 | +| Partial-Attack | 52 | 259 | 74 | 26 | 51.239 | +| Support | 668 | 488 | 167.266 | 38 | 82.222 | + +
+ Histogram (split: glaucoma_test, 100 documents) + +![img_5.png](img/rtd-label_abs-glu_test.png) + +
+ +##### mixed_test (100 documents) + +| | len | max | mean | min | std | +| :------------- | --: | --: | ------: | --: | ------: | +| ALL | 658 | 459 | 145.067 | 23 | 77.921 | +| Attack | 6 | 411 | 164 | 34 | 174.736 | +| Partial-Attack | 42 | 259 | 65.762 | 23 | 62.426 | +| Support | 610 | 459 | 150.341 | 35 | 74.273 | + +
+ Histogram (split: mixed_test, 100 documents) + +![img_6.png](img/rtd-label_abs-mix_test.png) + +
+ +#### Span lengths (tokens) + +The span length is measured from the first token of the first argumentative unit to the last token of the particular unit. + +We collect the following statistics: number of documents in the split (*no. doc*), no. of spans (*len*), mean of number of tokens in a span (*mean*), standard deviation of the number of tokens (*std*), minimum tokens in a span (*min*), and maximum tokens in a span (*max*). +We also present histograms in the collasible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly. + +
+Command + +``` +python src/evaluate_documents.py dataset=abstrct_base metric=span_lengths_tokens +``` + +
+ +| statistics | neoplasm_train | neoplasm_dev | neoplasm_test | glaucoma_test | mixed_test | +| :--------- | -------------: | -----------: | ------------: | ------------: | ---------: | +| no. doc | 350 | 50 | 100 | 100 | 100 | +| len | 2267 | 326 | 686 | 594 | 600 | +| mean | 34.303 | 37.135 | 32.566 | 38.997 | 38.507 | +| std | 22.425 | 29.941 | 20.264 | 22.604 | 24.036 | +| min | 5 | 5 | 6 | 6 | 7 | +| max | 250 | 288 | 182 | 169 | 159 | + +
+ Histogram (split: neoplasm_train, 350 documents) + +![slt_abs-neo_train.png](img%2Fspan_len_token%2Fslt_abs-neo_train.png) + +
+
+ Histogram (split: neoplasm_dev, 50 documents) + +![slt_abs-neo_dev.png](img%2Fspan_len_token%2Fslt_abs-neo_dev.png) + +
+
+ Histogram (split: neoplasm_test, 100 documents) + +![slt_abs-neo_test.png](img%2Fspan_len_token%2Fslt_abs-neo_test.png) + +
+
+ Histogram (split: glucoma_test, 100 documents) + +![slt_abs-glu_test.png](img%2Fspan_len_token%2Fslt_abs-glu_test.png) + +
+
+ Histogram (split: mixed_test, 100 documents) + +![slt_abs-mix_test.png](img%2Fspan_len_token%2Fslt_abs-mix_test.png) + +
+ +#### Text length (tokens) + +The text (document) length is measured from the first token of the document to the last one. + +We collect the following statistics: number of documents in the split (*no. doc*), mean of document token-length (*mean*), standard deviation of the length (*std*), minimum number of tokens in a document (*min*), and maximum number of tokens in a document (*max*). +We also present histograms in the collasible, showing the distribution of these text lengths (x-axis; and unit-counts in y-axis), accordingly. + +
+Command + +``` +python src/evaluate_documents.py dataset=abstrct_base metric=count_text_tokens +``` + +
+ +| statistics | neoplasm_train | neoplasm_dev | neoplasm_test | glaucoma_test | mixed_test | +| :--------- | -------------: | -----------: | ------------: | ------------: | ---------: | +| no. doc | 350 | 50 | 100 | 100 | 100 | +| mean | 447.291 | 481.66 | 442.79 | 456.78 | 450.29 | +| std | 91.266 | 116.239 | 89.692 | 115.535 | 87.002 | +| min | 301 | 329 | 292 | 212 | 268 | +| max | 843 | 952 | 776 | 1022 | 776 | + +
+ Histogram (split: neoplasm_train, 350 documents) + +![tl_abs-neo_train.png](img%2Ftoken_len%2Ftl_abs-neo_train.png) + +
+
+ Histogram (split: neoplasm_dev, 50 documents) + +![tl_abs-neo_dev.png](img%2Ftoken_len%2Ftl_abs-neo_dev.png) + +
+
+ Histogram (split: neoplasm_test, 100 documents) + +![tl_abs-neo_test.png](img%2Ftoken_len%2Ftl_abs-neo_test.png) + +
+
+ Histogram (split: glucoma_test, 100 documents) + +![tl_abs-glu_test.png](img%2Ftoken_len%2Ftl_abs-glu_test.png) + +
+
+ Histogram (split: mixed_test, 100 documents) + +![tl_abs-mix_test.png](img%2Ftoken_len%2Ftl_abs-mix_test.png) + +
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[PATCH 18/42] edited text in abstrct/readme --- dataset_builders/pie/abstrct/README.md | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/dataset_builders/pie/abstrct/README.md b/dataset_builders/pie/abstrct/README.md index 0a07da6b..e7f00bcc 100644 --- a/dataset_builders/pie/abstrct/README.md +++ b/dataset_builders/pie/abstrct/README.md @@ -111,8 +111,16 @@ Morio et al. ([2022](https://aclanthology.org/2022.tacl-1.37.pdf); p. 642, Table ### Collected Statistics after Document Conversion -In this section, we collect further statistics of the dataset after the conversion to `TextDocumentWithLabeledSpansAndBinaryRelations`. -In the commands, we used the following dataset config: `configs/dataset/abstrct_base.yaml`: +We use the script `evaluate_documents.py` from [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1) to generate these statistics. +After checking out that code, the statistics and plots can be generated by the command: + +```commandline +python src/evaluate_documents.py dataset=abstrct_base metric=METRIC +``` + +where a `METRIC` is called according to the available metric configs in `config/metric/METRIC` (see [metrics](https://github.com/ArneBinder/pytorch-ie-hydra-template-1/tree/main/configs/metric)). + +This also requires to have the following dataset config in `configs/dataset/abstrct_base.yaml` of this dataset within the repo directory: ```commandline _target_: src.utils.execute_pipeline @@ -122,10 +130,7 @@ input: revision: 277dc703fd78614635e86fe57c636b54931538b2 ``` -The script `evaluate_documents.py` comes from [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1). - -For the tokenization, we use `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased)) -to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). +For token based metrics, this uses `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased) to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). #### Relation argument (outer) token distance per label @@ -134,7 +139,7 @@ The distance is measured from the first token of the first argumentative unit to We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. -*Note that*: to collect the relation argument distance by tokens, the [respective branch](https://github.com/ArneBinder/pytorch-ie-hydra-template-1/pull/135) must be checked out instead of the `main` branch. +**Important**: To reproduce the statistics and figures for this metric by yourself, the [`relation_argument_distance_collector` branch](https://github.com/ArneBinder/pytorch-ie-hydra-template-1/pull/135) of `pytorch-ie-hydra-template` needs to be checked out instead of the `main` branch!
Command From 19d31a50dc7c1178f0aa65fe5b902f821a48dedb Mon Sep 17 00:00:00 2001 From: Ruangrin L <88072261+idalr@users.noreply.github.com> Date: Mon, 11 Mar 2024 15:07:21 +0100 Subject: [PATCH 19/42] minor edit abstrct/readme --- dataset_builders/pie/abstrct/README.md | 30 ++++++++++++-------------- 1 file changed, 14 insertions(+), 16 deletions(-) diff --git a/dataset_builders/pie/abstrct/README.md b/dataset_builders/pie/abstrct/README.md index e7f00bcc..95487edc 100644 --- a/dataset_builders/pie/abstrct/README.md +++ b/dataset_builders/pie/abstrct/README.md @@ -139,13 +139,11 @@ The distance is measured from the first token of the first argumentative unit to We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. -**Important**: To reproduce the statistics and figures for this metric by yourself, the [`relation_argument_distance_collector` branch](https://github.com/ArneBinder/pytorch-ie-hydra-template-1/pull/135) of `pytorch-ie-hydra-template` needs to be checked out instead of the `main` branch! -
Command ``` -python src/evaluate_documents.py dataset=abstrct_base metric=count_relation_argument_distances +python src/evaluate_documents.py dataset=abstrct_base metric=relation_argument_token_distances ```
@@ -258,40 +256,40 @@ python src/evaluate_documents.py dataset=abstrct_base metric=span_lengths_tokens
Histogram (split: neoplasm_train, 350 documents) -![slt_abs-neo_train.png](img%2Fspan_len_token%2Fslt_abs-neo_train.png) +![slt_abs-neo_train.png](img%2Fslt_abs-neo_train.png)
Histogram (split: neoplasm_dev, 50 documents) -![slt_abs-neo_dev.png](img%2Fspan_len_token%2Fslt_abs-neo_dev.png) +![slt_abs-neo_dev.png](img%2Fslt_abs-neo_dev.png)
Histogram (split: neoplasm_test, 100 documents) -![slt_abs-neo_test.png](img%2Fspan_len_token%2Fslt_abs-neo_test.png) +![slt_abs-neo_test.png](img%2Fslt_abs-neo_test.png)
Histogram (split: glucoma_test, 100 documents) -![slt_abs-glu_test.png](img%2Fspan_len_token%2Fslt_abs-glu_test.png) +![slt_abs-glu_test.png](img%2Fslt_abs-glu_test.png)
Histogram (split: mixed_test, 100 documents) -![slt_abs-mix_test.png](img%2Fspan_len_token%2Fslt_abs-mix_test.png) +![slt_abs-mix_test.png](img%2Fslt_abs-mix_test.png)
-#### Text length (tokens) +#### Token length (tokens) -The text (document) length is measured from the first token of the document to the last one. +The token length is measured from the first token of the document to the last one. We collect the following statistics: number of documents in the split (*no. doc*), mean of document token-length (*mean*), standard deviation of the length (*std*), minimum number of tokens in a document (*min*), and maximum number of tokens in a document (*max*). -We also present histograms in the collasible, showing the distribution of these text lengths (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collasible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly.
Command @@ -313,31 +311,31 @@ python src/evaluate_documents.py dataset=abstrct_base metric=count_text_tokens
Histogram (split: neoplasm_train, 350 documents) -![tl_abs-neo_train.png](img%2Ftoken_len%2Ftl_abs-neo_train.png) +![tl_abs-neo_train.png](img%2Ftl_abs-neo_train.png)
Histogram (split: neoplasm_dev, 50 documents) -![tl_abs-neo_dev.png](img%2Ftoken_len%2Ftl_abs-neo_dev.png) +![tl_abs-neo_dev.png](img%2Ftl_abs-neo_dev.png)
Histogram (split: neoplasm_test, 100 documents) -![tl_abs-neo_test.png](img%2Ftoken_len%2Ftl_abs-neo_test.png) +![tl_abs-neo_test.png](img%2Ftl_abs-neo_test.png)
Histogram (split: glucoma_test, 100 documents) -![tl_abs-glu_test.png](img%2Ftoken_len%2Ftl_abs-glu_test.png) +![tl_abs-glu_test.png](img%2Ftl_abs-glu_test.png)
Histogram (split: mixed_test, 100 documents) -![tl_abs-mix_test.png](img%2Ftoken_len%2Ftl_abs-mix_test.png) +![tl_abs-mix_test.png](img%2Ftl_abs-mix_test.png)
From 7a117711b508b0f8b0c9b5110f9dccdb97a7fb18 Mon Sep 17 00:00:00 2001 From: Ruangrin L <88072261+idalr@users.noreply.github.com> Date: Mon, 11 Mar 2024 15:07:52 +0100 Subject: [PATCH 20/42] removed abstrct histograms from statistics folder --- .../rtd-label_abs-glu_test.png | Bin 17093 -> 0 bytes .../rtd-label_abs-mix_test.png | Bin 15611 -> 0 bytes .../rtd-label_abs-neo_dev.png | Bin 14896 -> 0 bytes .../rtd-label_abs-neo_test.png | Bin 17181 -> 0 bytes .../rtd-label_abs-neo_train.png | Bin 17315 -> 0 bytes .../img/span_len_token/slt_abs-glu_test.png | Bin 11444 -> 0 bytes .../img/span_len_token/slt_abs-mix_test.png | Bin 12819 -> 0 bytes .../img/span_len_token/slt_abs-neo_dev.png | Bin 12192 -> 0 bytes .../img/span_len_token/slt_abs-neo_test.png | Bin 11118 -> 0 bytes .../img/span_len_token/slt_abs-neo_train.png | Bin 13025 -> 0 bytes AM_statistics/img/token_len/tl_abs-glu_test.png | Bin 11715 -> 0 bytes AM_statistics/img/token_len/tl_abs-mix_test.png | Bin 12049 -> 0 bytes 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21/42] moved content and files to aae2.readme, and removed from statistics folder --- ...argument_outer_token_distance_per_label.md | 4 +- dataset_builders/pie/aae2/README.md | 159 +++++++++++++++++- .../pie/aae2/img}/rtd-label_aae2_test.png | Bin .../pie/aae2/img}/rtd-label_aae2_train.png | Bin 4 files changed, 155 insertions(+), 8 deletions(-) rename {AM_statistics/img/relation_token_distance_per_label => dataset_builders/pie/aae2/img}/rtd-label_aae2_test.png (100%) rename {AM_statistics/img/relation_token_distance_per_label => dataset_builders/pie/aae2/img}/rtd-label_aae2_train.png (100%) diff --git a/AM_statistics/Relation_argument_outer_token_distance_per_label.md b/AM_statistics/Relation_argument_outer_token_distance_per_label.md index 4376ac17..61004821 100644 --- a/AM_statistics/Relation_argument_outer_token_distance_per_label.md +++ b/AM_statistics/Relation_argument_outer_token_distance_per_label.md @@ -28,7 +28,7 @@ The script `evaluate_documents.py` comes from [PyTorch-IE-Hydra-Template](https:
Histogram (split: train, 322 documents) -![img_1.png](img/relation_token_distance_per_label/rtd-label_aae2_train.png) +![img_1.png](../dataset_builders/pie/aae2/img/rtd-label_aae2_train.png)
@@ -44,7 +44,7 @@ The script `evaluate_documents.py` comes from [PyTorch-IE-Hydra-Template](https:
Histogram (split: test, 80 documents) -![img.png](img/relation_token_distance_per_label/rtd-label_aae2_test.png) +![img.png](../dataset_builders/pie/aae2/img/rtd-label_aae2_test.png)
diff --git a/dataset_builders/pie/aae2/README.md b/dataset_builders/pie/aae2/README.md index f47a7f9f..47efc382 100644 --- a/dataset_builders/pie/aae2/README.md +++ b/dataset_builders/pie/aae2/README.md @@ -8,8 +8,7 @@ Therefore, the `aae2` dataset as described here follows the data structure from Argument Annotated Essays Corpus (AAEC) ([Stab and Gurevych, 2017](https://aclanthology.org/J17-3005.pdf)) contains student essays. A stance for a controversial theme is expressed by a major claim component as well as claim components, and premise components justify or refute the claims. Attack and support labels are defined as relations. The span covers a statement, *which can stand in isolation as a complete sentence*, according to the AAEC annotation guidelines. All components are annotated with minimum boundaries of a clause or sentence excluding so-called "shell" language such as *On the other hand* and *Hence*. (Morio et al., 2022, p. 642) -In the original dataset, there is no premise that links to another premise or claim in a different paragraph. That means, an argumentation tree structure is complete within each paragraph. Therefore, it is possible to train a model on the full documents or just at the paragraph-level which is usually less memory-exhaustive (Eger et al., 2017, p. 16). -However, through our `DOCUMENT_CONVERTERS`, we build links between claims, creating a graph structure throughout an entire essay (see [Document Converters](#document-converters)). +There is no premise that links to another premise or claim in a different paragraph. That means, an argumentation tree structure is complete within each paragraph. Therefore, it is possible to train a model on the full documents or just at the paragraph-level which is usually less memory-exhaustive (Eger et al., 2017, p. 16). ### Supported Tasks and Leaderboards @@ -51,7 +50,7 @@ assert isinstance(doc, builders.brat.BratDocumentWithMergedSpans) See further statistics in Stab & Gurevych (2017), p. 650, Table A.1. -### Label Descriptions and Statistics +### Label Descriptions #### Components @@ -65,6 +64,8 @@ See further statistics in Stab & Gurevych (2017), p. 650, Table A.1. - `Claim` constitutes the central component of each argument. Each one has at least one premise and takes stance attribute values "for" or "against" with regarding the major claim. - `Premise` is the reasons of the argument; either linked to claim or another premise. +**Note that** relations between `MajorClaim` and `Claim` were not annotated; however, each claim is annotated with an `Attribute` annotation with value `for` or `against` - which indicates the relation between itself and `MajorClaim`. In addition, when two non-related `Claim` 's appear in one paragraph, there is also no relations to one another. + #### Relations | Relations | Count | Percentage | @@ -78,8 +79,6 @@ See further statistics in Stab & Gurevych (2017), p. 650, Table A.1. See further description in Stab & Gurevych 2017, p.627 and the [annotation guideline](https://github.com/ArneBinder/pie-datasets/blob/db94035602610cefca2b1678aa2fe4455c96155d/data/datasets/ArgumentAnnotatedEssays-2.0/guideline.pdf). -**Note that** relations between `MajorClaim` and `Claim` were not annotated; however, each claim is annotated with an `Attribute` annotation with value `for` or `against` - which indicates the relation between itself and `MajorClaim`. In addition, when two non-related `Claim` 's appear in one paragraph, there is also no relations to one another. An example of a document is shown here below. - #### Example ![Example](img/sg17f2.png) @@ -109,7 +108,7 @@ The dataset provides document converters for the following target document types See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py) for the document type definitions. -#### Label Statistics after Document Conversion +#### Relation Label Statistics after Document Conversion When converting from `BratDocumentWithMergedSpan` to `TextDocumentWithLabeledSpansAndBinaryRelations` and `TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions`, we apply a relation-conversion method (see above) that changes the label counts for the relations, as follows: @@ -129,6 +128,154 @@ we apply a relation-conversion method (see above) that changes the label counts | support: `supports` | 5958 | 89.3 % | | attack: `attacks` | 715 | 10.7 % | +### Collected Statistics after Document Conversion + +We use the script `evaluate_documents.py` from [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1) to generate these statistics. +After checking out that code, the statistics and plots can be generated by the command: + +```commandline +python src/evaluate_documents.py dataset=aae2_base metric=METRIC +``` + +where a `METRIC` is called according to the available metric configs in `config/metric/METRIC` (see [metrics](https://github.com/ArneBinder/pytorch-ie-hydra-template-1/tree/main/configs/metric)). + +This also requires to have the following dataset config in `configs/dataset/abstrct_base.yaml` of this dataset within the repo directory: + +```commandline +_target_: src.utils.execute_pipeline +input: + _target_: pie_datasets.DatasetDict.load_dataset + path: pie/aae2 + revision: 1015ee38bd8a36549b344008f7a49af72956a7fe +``` + +For token based metrics, this uses `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased) to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). + +For relation-label statistics, we collect those from the default relation conversion method, i.e., `connect_first`, resulting in three distinct relation labels. + +#### Relation argument (outer) token distance per label + +The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance. + +We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). +We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. + +
+Command + +``` +python src/evaluate_documents.py dataset=aae2_base metric=relation_argument_token_distances +``` + +
+ +##### train (322 documents) + +| | len | max | mean | min | std | +| :---------------- | ---: | --: | ------: | --: | ------: | +| ALL | 9002 | 514 | 102.582 | 9 | 93.76 | +| attacks | 810 | 442 | 127.622 | 10 | 109.283 | +| semantically_same | 552 | 514 | 301.638 | 25 | 73.756 | +| supports | 7640 | 493 | 85.545 | 9 | 74.023 | + +
+ Histogram (split: train, 322 documents) + +![rtd-label_aae2_train.png](img%2Frtd-label_aae2_train.png) + +
+ +##### test (80 documents) + +| | len | max | mean | min | std | +| :---------------- | ---: | --: | ------: | --: | -----: | +| ALL | 2372 | 442 | 100.711 | 10 | 92.698 | +| attacks | 184 | 402 | 115.891 | 12 | 98.751 | +| semantically_same | 146 | 442 | 299.671 | 34 | 72.921 | +| supports | 2042 | 437 | 85.118 | 10 | 75.023 | + +
+ Histogram (split: test, 80 documents) + +![rtd-label_aae2_test.png](img%2Frtd-label_aae2_test.png) + +
+ +#### Span lengths (tokens) + +The span length is measured from the first token of the first argumentative unit to the last token of the particular unit. + +We collect the following statistics: number of documents in the split (*no. doc*), no. of spans (*len*), mean of number of tokens in a span (*mean*), standard deviation of the number of tokens (*std*), minimum tokens in a span (*min*), and maximum tokens in a span (*max*). +We also present histograms in the collasible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly. + +
+Command + +``` +python src/evaluate_documents.py dataset=aae2_base metric=span_lengths_tokens +``` + +
+ +| statistics | train | test | +| :--------- | -----: | -----: | +| no. doc | 322 | 80 | +| len | 4823 | 1266 | +| mean | 17.157 | 16.317 | +| std | 8.079 | 7.953 | +| min | 3 | 3 | +| max | 75 | 50 | + +
+ Histogram (split: train, 332 documents) + +![slt_aae2_train.png](img%2Fslt_aae2_train.png) + +
+
+ Histogram (split: test, 80 documents) + +![slt_aae2_test.png](img%2Fslt_aae2_test.png) + +
+ +#### Token length (tokens) + +The token length is measured from the first token of the document to the last one. + +We collect the following statistics: number of documents in the split (*no. doc*), mean of document token-length (*mean*), standard deviation of the length (*std*), minimum number of tokens in a document (*min*), and maximum number of tokens in a document (*max*). +We also present histograms in the collasible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly. + +
+Command + +``` +python src/evaluate_documents.py dataset=aae2_base metric=count_text_tokens +``` + +
+ +| statistics | train | test | +| :--------- | ------: | -----: | +| no. doc | 322 | 80 | +| mean | 377.686 | 378.4 | +| std | 64.534 | 66.054 | +| min | 236 | 269 | +| max | 580 | 532 | + +
+ Histogram (split: train, 332 documents) + +![tl_aae2_train.png](img%2Ftl_aae2_train.png) + +
+
+ Histogram (split: test, 80 documents) + +![tl_aae2_test.png](img%2Ftl_aae2_test.png) + +
+ ## Dataset Creation ### Curation Rationale diff --git a/AM_statistics/img/relation_token_distance_per_label/rtd-label_aae2_test.png b/dataset_builders/pie/aae2/img/rtd-label_aae2_test.png similarity index 100% rename from AM_statistics/img/relation_token_distance_per_label/rtd-label_aae2_test.png rename to dataset_builders/pie/aae2/img/rtd-label_aae2_test.png diff --git a/AM_statistics/img/relation_token_distance_per_label/rtd-label_aae2_train.png b/dataset_builders/pie/aae2/img/rtd-label_aae2_train.png similarity index 100% rename from AM_statistics/img/relation_token_distance_per_label/rtd-label_aae2_train.png rename to dataset_builders/pie/aae2/img/rtd-label_aae2_train.png From 51e84bc5fe8e773d5c276497161926d9b2b348b7 Mon Sep 17 00:00:00 2001 From: Ruangrin L <88072261+idalr@users.noreply.github.com> Date: Mon, 11 Mar 2024 15:31:16 +0100 Subject: [PATCH 22/42] minor fix --- .../pie/aae2/img}/slt_aae2_test.png | Bin .../pie/aae2/img}/slt_aae2_train.png | Bin .../pie/aae2/img}/tl_aae2_test.png | Bin .../pie/aae2/img}/tl_aae2_train.png | Bin 4 files changed, 0 insertions(+), 0 deletions(-) rename {AM_statistics/img/span_len_token => dataset_builders/pie/aae2/img}/slt_aae2_test.png (100%) rename {AM_statistics/img/span_len_token => dataset_builders/pie/aae2/img}/slt_aae2_train.png (100%) rename {AM_statistics/img/token_len => dataset_builders/pie/aae2/img}/tl_aae2_test.png (100%) rename {AM_statistics/img/token_len => dataset_builders/pie/aae2/img}/tl_aae2_train.png (100%) diff --git a/AM_statistics/img/span_len_token/slt_aae2_test.png b/dataset_builders/pie/aae2/img/slt_aae2_test.png similarity index 100% rename from AM_statistics/img/span_len_token/slt_aae2_test.png rename to dataset_builders/pie/aae2/img/slt_aae2_test.png diff --git a/AM_statistics/img/span_len_token/slt_aae2_train.png b/dataset_builders/pie/aae2/img/slt_aae2_train.png similarity index 100% rename from AM_statistics/img/span_len_token/slt_aae2_train.png rename to dataset_builders/pie/aae2/img/slt_aae2_train.png diff --git a/AM_statistics/img/token_len/tl_aae2_test.png b/dataset_builders/pie/aae2/img/tl_aae2_test.png similarity index 100% rename from AM_statistics/img/token_len/tl_aae2_test.png rename to dataset_builders/pie/aae2/img/tl_aae2_test.png diff --git a/AM_statistics/img/token_len/tl_aae2_train.png b/dataset_builders/pie/aae2/img/tl_aae2_train.png similarity index 100% rename from AM_statistics/img/token_len/tl_aae2_train.png rename to dataset_builders/pie/aae2/img/tl_aae2_train.png From 0c8efdf8ee4a119759d8baebb47e1742db57d484 Mon Sep 17 00:00:00 2001 From: Ruangrin L <88072261+idalr@users.noreply.github.com> Date: Mon, 11 Mar 2024 16:17:09 +0100 Subject: [PATCH 23/42] minor edit --- dataset_builders/pie/aae2/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dataset_builders/pie/aae2/README.md b/dataset_builders/pie/aae2/README.md index 47efc382..434cd4d3 100644 --- a/dataset_builders/pie/aae2/README.md +++ b/dataset_builders/pie/aae2/README.md @@ -139,7 +139,7 @@ python src/evaluate_documents.py dataset=aae2_base metric=METRIC where a `METRIC` is called according to the available metric configs in `config/metric/METRIC` (see [metrics](https://github.com/ArneBinder/pytorch-ie-hydra-template-1/tree/main/configs/metric)). -This also requires to have the following dataset config in `configs/dataset/abstrct_base.yaml` of this dataset within the repo directory: +This also requires to have the following dataset config in `configs/dataset/aae2_base.yaml` of this dataset within the repo directory: ```commandline _target_: src.utils.execute_pipeline From 01598ecbad91ca9a500e77a916ffef8a0fe30b99 Mon Sep 17 00:00:00 2001 From: Ruangrin L <88072261+idalr@users.noreply.github.com> Date: Mon, 11 Mar 2024 16:23:11 +0100 Subject: [PATCH 24/42] moved content and files to argmicro/readme.md --- dataset_builders/pie/argmicro/README.md | 123 ++++++++++++++++++ .../pie/argmicro/img}/rtd-label_argmicro.png | Bin .../pie/argmicro/img}/slt_argmicro.png | Bin .../pie/argmicro/img}/tl_argmicro.png | Bin 4 files changed, 123 insertions(+) rename {AM_statistics/img/relation_token_distance_per_label => dataset_builders/pie/argmicro/img}/rtd-label_argmicro.png (100%) rename {AM_statistics/img/span_len_token => dataset_builders/pie/argmicro/img}/slt_argmicro.png (100%) rename {AM_statistics/img/token_len => dataset_builders/pie/argmicro/img}/tl_argmicro.png (100%) diff --git a/dataset_builders/pie/argmicro/README.md b/dataset_builders/pie/argmicro/README.md index fe9da6f9..a0f78694 100644 --- a/dataset_builders/pie/argmicro/README.md +++ b/dataset_builders/pie/argmicro/README.md @@ -53,3 +53,126 @@ The dataset provides document converters for the following target document types See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py) for the document type definitions. + +### Collected Statistics after Document Conversion + +We use the script `evaluate_documents.py` from [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1) to generate these statistics. +After checking out that code, the statistics and plots can be generated by the command: + +```commandline +python src/evaluate_documents.py dataset=argmicro_base metric=METRIC +``` + +where a `METRIC` is called according to the available metric configs in `config/metric/METRIC` (see [metrics](https://github.com/ArneBinder/pytorch-ie-hydra-template-1/tree/main/configs/metric)). + +This also requires to have the following dataset config in `configs/dataset/argmicro_base.yaml` of this dataset within the repo directory: + +```commandline +_target_: src.utils.execute_pipeline +input: + _target_: pie_datasets.DatasetDict.load_dataset + path: pie/argmicro + revision: 28ef031d2a2c97be7e9ed360e1a5b20bd55b57b2 + name: en + base_dataset_kwargs: + data_dir: data/datasets/arg-microtexts-master.zip +``` + +For token based metrics, this uses `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased) to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). + +#### Relation argument (outer) token distance per label + +The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance. + +We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). +We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. + +
+Command + +``` +python src/evaluate_documents.py dataset=argmicro_base metric=relation_argument_token_distances +``` + +
+ +##### train (112 documents) + +| | len | max | mean | min | std | +| :---- | ---: | --: | -----: | --: | -----: | +| ALL | 1018 | 127 | 44.434 | 14 | 21.501 | +| exa | 18 | 63 | 33.556 | 16 | 13.056 | +| joint | 88 | 48 | 30.091 | 17 | 9.075 | +| reb | 220 | 127 | 49.327 | 16 | 24.653 | +| sup | 562 | 124 | 46.534 | 14 | 22.079 | +| und | 130 | 84 | 38.292 | 17 | 12.321 | + +
+ Histogram (split: train, 112 documents) + +![rtd-label_argmicro.png](img%2Frtd-label_argmicro.png) + +
+ +#### Span lengths (tokens) + +The span length is measured from the first token of the first argumentative unit to the last token of the particular unit. + +We collect the following statistics: number of documents in the split (*no. doc*), no. of spans (*len*), mean of number of tokens in a span (*mean*), standard deviation of the number of tokens (*std*), minimum tokens in a span (*min*), and maximum tokens in a span (*max*). +We also present histograms in the collasible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly. + +
+Command + +``` +python src/evaluate_documents.py dataset=argmicro_base metric=span_lengths_tokens +``` + +
+ +| statistics | train | +| :--------- | -----: | +| no. doc | 112 | +| len | 576 | +| mean | 16.365 | +| std | 6.545 | +| min | 4 | +| max | 41 | + +
+ Histogram (split: train, 112 documents) + +![slt_argmicro.png](img%2Fslt_argmicro.png) + +
+ +#### Token length (tokens) + +The token length is measured from the first token of the document to the last one. + +We collect the following statistics: number of documents in the split (*no. doc*), mean of document token-length (*mean*), standard deviation of the length (*std*), minimum number of tokens in a document (*min*), and maximum number of tokens in a document (*max*). +We also present histograms in the collasible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly. + +
+Command + +``` +python src/evaluate_documents.py dataset=argmicro_base metric=count_text_tokens +``` + +
+ +| statistics | train | +| :--------- | -----: | +| no. doc | 112 | +| mean | 84.161 | +| std | 22.596 | +| min | 36 | +| max | 153 | + +
+ Histogram (split: train, 112 documents) + +![tl_argmicro.png](img%2Ftl_argmicro.png) + +
diff --git a/AM_statistics/img/relation_token_distance_per_label/rtd-label_argmicro.png b/dataset_builders/pie/argmicro/img/rtd-label_argmicro.png similarity index 100% rename from AM_statistics/img/relation_token_distance_per_label/rtd-label_argmicro.png rename to dataset_builders/pie/argmicro/img/rtd-label_argmicro.png diff --git a/AM_statistics/img/span_len_token/slt_argmicro.png b/dataset_builders/pie/argmicro/img/slt_argmicro.png similarity index 100% rename from AM_statistics/img/span_len_token/slt_argmicro.png rename to dataset_builders/pie/argmicro/img/slt_argmicro.png diff --git a/AM_statistics/img/token_len/tl_argmicro.png b/dataset_builders/pie/argmicro/img/tl_argmicro.png similarity index 100% rename from AM_statistics/img/token_len/tl_argmicro.png rename to dataset_builders/pie/argmicro/img/tl_argmicro.png From 3a6d0f3436fb3c13f9f9f9ef102f446e0d778947 Mon Sep 17 00:00:00 2001 From: Ruangrin L <88072261+idalr@users.noreply.github.com> Date: Mon, 11 Mar 2024 16:29:44 +0100 Subject: [PATCH 25/42] moved content and files to cdcp/readme.md --- ...argument_outer_token_distance_per_label.md | 4 +- dataset_builders/pie/cdcp/README.md | 146 ++++++++++++++++++ .../pie/cdcp/img}/rtd-label_cdcp_test.png | Bin .../pie/cdcp/img}/rtd-label_cdcp_train.png | Bin .../pie/cdcp/img}/slt_cdcp_test.png | Bin .../pie/cdcp/img}/slt_cdcp_train.png | Bin .../pie/cdcp/img}/tl_cdcp_test.png | Bin .../pie/cdcp/img}/tl_cdcp_train.png | Bin 8 files changed, 148 insertions(+), 2 deletions(-) rename {AM_statistics/img/relation_token_distance_per_label => dataset_builders/pie/cdcp/img}/rtd-label_cdcp_test.png (100%) rename {AM_statistics/img/relation_token_distance_per_label => dataset_builders/pie/cdcp/img}/rtd-label_cdcp_train.png (100%) rename {AM_statistics/img/span_len_token => dataset_builders/pie/cdcp/img}/slt_cdcp_test.png (100%) rename {AM_statistics/img/span_len_token => dataset_builders/pie/cdcp/img}/slt_cdcp_train.png (100%) rename {AM_statistics/img/token_len => dataset_builders/pie/cdcp/img}/tl_cdcp_test.png (100%) rename {AM_statistics/img/token_len => dataset_builders/pie/cdcp/img}/tl_cdcp_train.png (100%) diff --git a/AM_statistics/Relation_argument_outer_token_distance_per_label.md b/AM_statistics/Relation_argument_outer_token_distance_per_label.md index 61004821..a7e9aa8e 100644 --- a/AM_statistics/Relation_argument_outer_token_distance_per_label.md +++ b/AM_statistics/Relation_argument_outer_token_distance_per_label.md @@ -188,7 +188,7 @@ python src/evaluate_documents.py dataset=argmicro_base metric=count_relation_arg
Histogram (split: train, 580 documents) -![img_8.png](img/relation_token_distance_per_label/rtd-label_cdcp_train.png) +![img_8.png](../dataset_builders/pie/cdcp/img/rtd-label_cdcp_train.png)
@@ -203,7 +203,7 @@ python src/evaluate_documents.py dataset=argmicro_base metric=count_relation_arg
Histogram (split: test, 150 documents) -![img_9.png](img/relation_token_distance_per_label/rtd-label_cdcp_test.png) +![img_9.png](../dataset_builders/pie/cdcp/img/rtd-label_cdcp_test.png)
diff --git a/dataset_builders/pie/cdcp/README.md b/dataset_builders/pie/cdcp/README.md index cc3e8bd3..c90e968c 100644 --- a/dataset_builders/pie/cdcp/README.md +++ b/dataset_builders/pie/cdcp/README.md @@ -32,3 +32,149 @@ The dataset provides document converters for the following target document types See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py) for the document type definitions. + +### Collected Statistics after Document Conversion + +We use the script `evaluate_documents.py` from [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1) to generate these statistics. +After checking out that code, the statistics and plots can be generated by the command: + +```commandline +python src/evaluate_documents.py dataset=cdcp_base metric=METRIC +``` + +where a `METRIC` is called according to the available metric configs in `config/metric/METRIC` (see [metrics](https://github.com/ArneBinder/pytorch-ie-hydra-template-1/tree/main/configs/metric)). + +This also requires to have the following dataset config in `configs/dataset/cdcp_base.yaml` of this dataset within the repo directory: + +```commandline +_target_: src.utils.execute_pipeline +input: + _target_: pie_datasets.DatasetDict.load_dataset + path: pie/cdcp + revision: 001722894bdca6df6a472d0d186a3af103e392c5 + base_dataset_kwargs: + data_dir: data/datasets/cdcp_acl17.zip +``` + +For token based metrics, this uses `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased) to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). + +#### Relation argument (outer) token distance per label + +The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance. + +We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). +We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. + +
+Command + +``` +python src/evaluate_documents.py dataset=cdcp_base metric=relation_argument_token_distances +``` + +
+ +##### train (580 documents) + +| | len | max | mean | min | std | +| :------- | ---: | --: | -----: | --: | -----: | +| ALL | 2204 | 240 | 48.839 | 8 | 31.462 | +| evidence | 94 | 196 | 66.723 | 14 | 42.444 | +| reason | 2110 | 240 | 48.043 | 8 | 30.64 | + +
+ Histogram (split: train, 580 documents) + +![rtd-label_cdcp_train.png](img%2Frtd-label_cdcp_train.png) + +
+ +##### test (150 documents) + +| | len | max | mean | min | std | +| :------- | --: | --: | -----: | --: | -----: | +| ALL | 648 | 212 | 51.299 | 8 | 31.159 | +| evidence | 52 | 170 | 73.923 | 20 | 39.855 | +| reason | 596 | 212 | 49.326 | 8 | 29.47 | + +
+ Histogram (split: test, 150 documents) + +![rtd-label_cdcp_test.png](img%2Frtd-label_cdcp_test.png) + +
+ +#### Span lengths (tokens) + +The span length is measured from the first token of the first argumentative unit to the last token of the particular unit. + +We collect the following statistics: number of documents in the split (*no. doc*), no. of spans (*len*), mean of number of tokens in a span (*mean*), standard deviation of the number of tokens (*std*), minimum tokens in a span (*min*), and maximum tokens in a span (*max*). +We also present histograms in the collasible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly. + +
+Command + +``` +python src/evaluate_documents.py dataset=cdcp_base metric=span_lengths_tokens +``` + +
+ +| statistics | train | test | +| :--------- | -----: | -----: | +| no. doc | 580 | 150 | +| len | 3901 | 1026 | +| mean | 19.441 | 18.758 | +| std | 11.71 | 10.388 | +| min | 2 | 3 | +| max | 142 | 83 | + +
+ Histogram (split: train, 580 documents) + +![slt_cdcp_train.png](img%2Fslt_cdcp_train.png) + +
+
+ Histogram (split: test, 150 documents) + +![slt_cdcp_test.png](img%2Fslt_cdcp_test.png) + +
+ +#### Token length (tokens) + +The token length is measured from the first token of the document to the last one. + +We collect the following statistics: number of documents in the split (*no. doc*), mean of document token-length (*mean*), standard deviation of the length (*std*), minimum number of tokens in a document (*min*), and maximum number of tokens in a document (*max*). +We also present histograms in the collasible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly. + +
+Command + +``` +python src/evaluate_documents.py dataset=cdcp_base metric=count_text_tokens +``` + +
+ +| statistics | train | test | +| :--------- | ------: | ------: | +| no. doc | 580 | 150 | +| mean | 130.781 | 128.673 | +| std | 101.121 | 98.708 | +| min | 13 | 15 | +| max | 562 | 571 | + +
+ Histogram (split: train, 580 documents) + +![tl_cdcp_train.png](img%2Ftl_cdcp_train.png) + +
+
+ Histogram (split: test, 150 documents) + +![tl_cdcp_test.png](img%2Ftl_cdcp_test.png) + +
diff --git a/AM_statistics/img/relation_token_distance_per_label/rtd-label_cdcp_test.png b/dataset_builders/pie/cdcp/img/rtd-label_cdcp_test.png similarity index 100% rename from AM_statistics/img/relation_token_distance_per_label/rtd-label_cdcp_test.png rename to dataset_builders/pie/cdcp/img/rtd-label_cdcp_test.png diff --git a/AM_statistics/img/relation_token_distance_per_label/rtd-label_cdcp_train.png b/dataset_builders/pie/cdcp/img/rtd-label_cdcp_train.png similarity index 100% rename from AM_statistics/img/relation_token_distance_per_label/rtd-label_cdcp_train.png rename to dataset_builders/pie/cdcp/img/rtd-label_cdcp_train.png diff --git a/AM_statistics/img/span_len_token/slt_cdcp_test.png b/dataset_builders/pie/cdcp/img/slt_cdcp_test.png similarity index 100% rename from AM_statistics/img/span_len_token/slt_cdcp_test.png rename to dataset_builders/pie/cdcp/img/slt_cdcp_test.png diff --git a/AM_statistics/img/span_len_token/slt_cdcp_train.png b/dataset_builders/pie/cdcp/img/slt_cdcp_train.png similarity index 100% rename from AM_statistics/img/span_len_token/slt_cdcp_train.png rename to dataset_builders/pie/cdcp/img/slt_cdcp_train.png diff --git a/AM_statistics/img/token_len/tl_cdcp_test.png b/dataset_builders/pie/cdcp/img/tl_cdcp_test.png similarity index 100% rename from AM_statistics/img/token_len/tl_cdcp_test.png rename to dataset_builders/pie/cdcp/img/tl_cdcp_test.png diff --git a/AM_statistics/img/token_len/tl_cdcp_train.png b/dataset_builders/pie/cdcp/img/tl_cdcp_train.png similarity index 100% rename from AM_statistics/img/token_len/tl_cdcp_train.png rename to dataset_builders/pie/cdcp/img/tl_cdcp_train.png From 6105b6117d49f86162adccaa640cf2e4dba254d1 Mon Sep 17 00:00:00 2001 From: Ruangrin L <88072261+idalr@users.noreply.github.com> Date: Mon, 11 Mar 2024 16:36:57 +0100 Subject: [PATCH 26/42] minor fix --- dataset_builders/pie/argmicro/README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/dataset_builders/pie/argmicro/README.md b/dataset_builders/pie/argmicro/README.md index a0f78694..b1b281a0 100644 --- a/dataset_builders/pie/argmicro/README.md +++ b/dataset_builders/pie/argmicro/README.md @@ -96,8 +96,6 @@ python src/evaluate_documents.py dataset=argmicro_base metric=relation_argument_
-##### train (112 documents) - | | len | max | mean | min | std | | :---- | ---: | --: | -----: | --: | -----: | | ALL | 1018 | 127 | 44.434 | 14 | 21.501 | From 53afeab52e15c4ae6b9dd313e5092457e77c83b3 Mon Sep 17 00:00:00 2001 From: Ruangrin L <88072261+idalr@users.noreply.github.com> Date: Mon, 11 Mar 2024 16:37:15 +0100 Subject: [PATCH 27/42] moved content and files to scidtb_argmin/readme.md --- ...argument_outer_token_distance_per_label.md | 2 +- dataset_builders/pie/scidtb_argmin/README.md | 124 +++++++++++++++++- .../img}/rtd-label_scitdb-argmin.png | Bin .../scidtb_argmin/img}/slt_scitdb-argmin.png | Bin .../scidtb_argmin/img}/tl_scidtb-argmin.png | Bin 5 files changed, 119 insertions(+), 7 deletions(-) rename {AM_statistics/img/relation_token_distance_per_label => dataset_builders/pie/scidtb_argmin/img}/rtd-label_scitdb-argmin.png (100%) rename {AM_statistics/img/span_len_token => dataset_builders/pie/scidtb_argmin/img}/slt_scitdb-argmin.png (100%) rename {AM_statistics/img/token_len => dataset_builders/pie/scidtb_argmin/img}/tl_scidtb-argmin.png (100%) diff --git a/AM_statistics/Relation_argument_outer_token_distance_per_label.md b/AM_statistics/Relation_argument_outer_token_distance_per_label.md index a7e9aa8e..6ae7215b 100644 --- a/AM_statistics/Relation_argument_outer_token_distance_per_label.md +++ b/AM_statistics/Relation_argument_outer_token_distance_per_label.md @@ -255,7 +255,7 @@ python src/evaluate_documents.py dataset=sciarg_base metric=count_relation_argum
Histogram (split: train, 60 documents) -![img_11.png](img/relation_token_distance_per_label/rtd-label_scitdb-argmin.png) +![img_11.png](../dataset_builders/pie/scidtb_argmin/img/rtd-label_scitdb-argmin.png)
diff --git a/dataset_builders/pie/scidtb_argmin/README.md b/dataset_builders/pie/scidtb_argmin/README.md index e5235c75..28f04d8a 100644 --- a/dataset_builders/pie/scidtb_argmin/README.md +++ b/dataset_builders/pie/scidtb_argmin/README.md @@ -7,7 +7,7 @@ This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for t The document type for this dataset is `SciDTBArgminDocument` which defines the following data fields: -- `tokens` (tuple of string) +- `tokens` (Tuple of string) - `id` (str, optional) - `metadata` (dictionary, optional) @@ -23,11 +23,123 @@ See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/a The dataset provides document converters for the following target document types: - `pytorch_ie.documents.TextDocumentWithLabeledSpansAndBinaryRelations` - - `labeled_spans`: `LabeledSpan` annotations, converted from`SciDTBArgminDocument`'s `units` - - labels: `proposal`, `assertion`, `result`, `observation`, `means`, `description` - - tuples of `tokens` are joined with a whitespace to create `text` for `LabeledSpans` - - `binary_relations`: `BinaryRelation` annotations, converted from `SciDTBArgminDocument`'s `relations` - - labels: `support`, `attack`, `additional`, `detail`, `sequence` See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py) for the document type definitions. + +### Collected Statistics after Document Conversion + +We use the script `evaluate_documents.py` from [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1) to generate these statistics. +After checking out that code, the statistics and plots can be generated by the command: + +```commandline +python src/evaluate_documents.py dataset=scidtb_argmin_base metric=METRIC +``` + +where a `METRIC` is called according to the available metric configs in `config/metric/METRIC` (see [metrics](https://github.com/ArneBinder/pytorch-ie-hydra-template-1/tree/main/configs/metric)). + +This also requires to have the following dataset config in `configs/dataset/scidtb_argmin_base.yaml` of this dataset within the repo directory: + +```commandline +_target_: src.utils.execute_pipeline +input: + _target_: pie_datasets.DatasetDict.load_dataset + path: pie/sciarg + revision: 982d5682ba414ee13cf92cb93ec18fc8e78e2b81 +``` + +For token based metrics, this uses `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased) to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). + +#### Relation argument (outer) token distance per label + +The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance. + +We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). +We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. + +
+Command + +``` +python src/evaluate_documents.py dataset=scidtb_argmin_base metric=relation_argument_token_distances +``` + +
+ +| | len | max | mean | min | std | +| :--------- | --: | --: | -----: | --: | -----: | +| ALL | 586 | 277 | 75.239 | 21 | 40.312 | +| additional | 54 | 180 | 59.593 | 36 | 29.306 | +| detail | 258 | 163 | 65.62 | 22 | 29.21 | +| sequence | 22 | 93 | 59.727 | 38 | 17.205 | +| support | 252 | 277 | 89.794 | 21 | 48.118 | + +
+ Histogram (split: train, 60 documents) + +![rtd-label_scitdb-argmin.png](img%2Frtd-label_scitdb-argmin.png) + +
+ +#### Span lengths (tokens) + +The span length is measured from the first token of the first argumentative unit to the last token of the particular unit. + +We collect the following statistics: number of documents in the split (*no. doc*), no. of spans (*len*), mean of number of tokens in a span (*mean*), standard deviation of the number of tokens (*std*), minimum tokens in a span (*min*), and maximum tokens in a span (*max*). +We also present histograms in the collasible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly. + +
+Command + +``` +python src/evaluate_documents.py dataset=scidtb_argmin_base metric=span_lengths_tokens +``` + +
+ +| statistics | train | +| :--------- | -----: | +| no. doc | 60 | +| len | 353 | +| mean | 27.946 | +| std | 13.054 | +| min | 7 | +| max | 123 | + +
+ Histogram (split: train, 60 documents) + +![slt_scitdb-argmin.png](img%2Fslt_scitdb-argmin.png) + +
+ +#### Token length (tokens) + +The token length is measured from the first token of the document to the last one. + +We collect the following statistics: number of documents in the split (*no. doc*), mean of document token-length (*mean*), standard deviation of the length (*std*), minimum number of tokens in a document (*min*), and maximum number of tokens in a document (*max*). +We also present histograms in the collasible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly. + +
+Command + +``` +python src/evaluate_documents.py dataset=scidtb_argmin_base metric=count_text_tokens +``` + +
+ +| statistics | train | +| :--------- | ------: | +| no. doc | 60 | +| mean | 164.417 | +| std | 64.572 | +| min | 80 | +| max | 532 | + +
+ Histogram (split: train, 60 documents) + +![tl_scidtb-argmin.png](img%2Ftl_scidtb-argmin.png) + +
diff --git a/AM_statistics/img/relation_token_distance_per_label/rtd-label_scitdb-argmin.png b/dataset_builders/pie/scidtb_argmin/img/rtd-label_scitdb-argmin.png similarity index 100% rename from AM_statistics/img/relation_token_distance_per_label/rtd-label_scitdb-argmin.png rename to dataset_builders/pie/scidtb_argmin/img/rtd-label_scitdb-argmin.png diff --git a/AM_statistics/img/span_len_token/slt_scitdb-argmin.png b/dataset_builders/pie/scidtb_argmin/img/slt_scitdb-argmin.png similarity index 100% rename from AM_statistics/img/span_len_token/slt_scitdb-argmin.png rename to dataset_builders/pie/scidtb_argmin/img/slt_scitdb-argmin.png diff --git a/AM_statistics/img/token_len/tl_scidtb-argmin.png b/dataset_builders/pie/scidtb_argmin/img/tl_scidtb-argmin.png similarity index 100% rename from AM_statistics/img/token_len/tl_scidtb-argmin.png rename to dataset_builders/pie/scidtb_argmin/img/tl_scidtb-argmin.png From 4477c28bcd38aca3656ff1bb50c871cd6dc1a8a1 Mon Sep 17 00:00:00 2001 From: Ruangrin L <88072261+idalr@users.noreply.github.com> Date: Mon, 11 Mar 2024 16:42:35 +0100 Subject: [PATCH 28/42] minor fix --- dataset_builders/pie/scidtb_argmin/README.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/dataset_builders/pie/scidtb_argmin/README.md b/dataset_builders/pie/scidtb_argmin/README.md index 28f04d8a..d50718d8 100644 --- a/dataset_builders/pie/scidtb_argmin/README.md +++ b/dataset_builders/pie/scidtb_argmin/README.md @@ -44,8 +44,10 @@ This also requires to have the following dataset config in `configs/dataset/scid _target_: src.utils.execute_pipeline input: _target_: pie_datasets.DatasetDict.load_dataset - path: pie/sciarg - revision: 982d5682ba414ee13cf92cb93ec18fc8e78e2b81 + path: pie/scidtb_argmin + revision: 335a8e6168919d7f204c6920eceb96745dbd161b + base_dataset_kwargs: + data_dir: data/datasets/scidtb_argmin_annotations.tgz ``` For token based metrics, this uses `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased) to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). From 075737c253b2c10b7f14ef5a7b2b89daf3b575e8 Mon Sep 17 00:00:00 2001 From: Ruangrin L <88072261+idalr@users.noreply.github.com> Date: Mon, 11 Mar 2024 16:45:14 +0100 Subject: [PATCH 29/42] moved content and files to sciarg/readme.md --- dataset_builders/pie/sciarg/README.md | 143 +++++++++++++++++- .../pie/sciarg/img}/rtd-label_sciarg.png | Bin .../pie/sciarg/img}/slt_sciarg.png | Bin .../pie/sciarg/img}/tl_sciarg.png | Bin 4 files changed, 139 insertions(+), 4 deletions(-) rename {AM_statistics/img/relation_token_distance_per_label => dataset_builders/pie/sciarg/img}/rtd-label_sciarg.png (100%) rename {AM_statistics/img/span_len_token => dataset_builders/pie/sciarg/img}/slt_sciarg.png (100%) rename {AM_statistics/img/token_len => dataset_builders/pie/sciarg/img}/tl_sciarg.png (100%) diff --git a/dataset_builders/pie/sciarg/README.md b/dataset_builders/pie/sciarg/README.md index 5f8cfee9..c6e90b45 100644 --- a/dataset_builders/pie/sciarg/README.md +++ b/dataset_builders/pie/sciarg/README.md @@ -55,6 +55,26 @@ doc = datasets["train"][0] assert isinstance(doc, builders.brat.BratDocument) ``` +### Document Converters + +The dataset provides document converters for the following target document types: + +- `pytorch_ie.documents.TextDocumentWithLabeledSpansAndBinaryRelations` + - `LabeledSpans`, converted from `BratDocument`'s `spans` + - labels: `background_claim`, `own_claim`, `data` + - if `spans` contain whitespace at the beginning and/or the end, the whitespace are trimmed out. + - `BinraryRelations`, converted from `BratDocument`'s `relations` + - labels: `supports`, `contradicts`, `semantically_same`, `parts_of_same` + - if the `relations` label is `semantically_same` or `parts_of_same`, they are merged if they are the same arguments after sorting. +- `pytorch_ie.documents.TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions` + - `LabeledSpans`, as above + - `BinaryRelations`, as above + - `LabeledPartitions`, partitioned `BratDocument`'s `text`, according to the paragraph, using regex. + - labels: `title`, `abstract`, `H1` + +See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py) for the document type +definitions. + ### Data Splits The dataset consists of a single `train` split that has 40 documents. @@ -133,6 +153,13 @@ possibly since [Lauscher et al., 2018](https://aclanthology.org/W18-5206/) prese (*Annotation Guidelines*, pp. 4-6) +There are currently discrepancies in label counts between + +- previous report in [Lauscher et al., 2018](https://aclanthology.org/W18-5206/), p. 43), +- current report above here (labels counted in `BratDocument`'s); + +possibly since [Lauscher et al., 2018](https://aclanthology.org/W18-5206/) presents the numbers of the real argumentative components, whereas here discontinuous components are still split (marked with the `parts_of_same` helper relation) and, thus, count per fragment. + #### Examples ![sample1](img/leaannof3.png) @@ -143,9 +170,14 @@ Below: Subset of relations in `A01` ![sample2](img/sciarg-sam.png) -### Document Converters +### Collected Statistics after Document Conversion -The dataset provides document converters for the following target document types: +We use the script `evaluate_documents.py` from [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1) to generate these statistics. +After checking out that code, the statistics and plots can be generated by the command: + +```commandline +python src/evaluate_documents.py dataset=sciarg_base metric=METRIC +``` From `default` version: @@ -178,8 +210,111 @@ From `resolve_parts_of_same` version: - `labeled_partitions`, `LabeledSpan` annotations, created from splitting `BratDocument`'s `text` at new paragraph in `xml` format. - labels: `title`, `abstract`, `H1` -See [here](https://github.com/ArneBinder/pie-modules/blob/main/src/pie_modules/documents.py) for the document type -definitions. +This also requires to have the following dataset config in `configs/dataset/sciarg_base.yaml` of this dataset within the repo directory: + +```commandline +_target_: src.utils.execute_pipeline +input: + _target_: pie_datasets.DatasetDict.load_dataset + path: pie/sciarg + revision: 982d5682ba414ee13cf92cb93ec18fc8e78e2b81 +``` + +For token based metrics, this uses `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased) to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ArneBinder/pie-modules/blob/main/src/pie_modules/documents.py)). + +#### Relation argument (outer) token distance per label + +The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance. + +We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). +We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. + +
+Command + +``` +python src/evaluate_documents.py dataset=sciarg_base metric=relation_argument_token_distances +``` + +
+ +| | len | max | mean | min | std | +| :---------------- | ----: | ---: | ------: | --: | ------: | +| ALL | 15640 | 2864 | 30.524 | 3 | 45.351 | +| contradicts | 1392 | 238 | 32.565 | 6 | 19.771 | +| parts_of_same | 2594 | 374 | 28.18 | 3 | 26.845 | +| semantically_same | 84 | 2864 | 206.333 | 11 | 492.268 | +| supports | 11570 | 407 | 29.527 | 4 | 24.189 | + +
+ Histogram (split: train, 40 documents) + +![rtd-label_sciarg.png](img%2Frtd-label_sciarg.png) + +
+ +#### Span lengths (tokens) + +The span length is measured from the first token of the first argumentative unit to the last token of the particular unit. + +We collect the following statistics: number of documents in the split (*no. doc*), no. of spans (*len*), mean of number of tokens in a span (*mean*), standard deviation of the number of tokens (*std*), minimum tokens in a span (*min*), and maximum tokens in a span (*max*). +We also present histograms in the collasible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly. + +
+Command + +``` +python src/evaluate_documents.py dataset=sciarg_base metric=span_lengths_tokens +``` + +
+ +| statistics | train | +| :--------- | -----: | +| no. doc | 40 | +| len | 13586 | +| mean | 11.677 | +| std | 8.731 | +| min | 1 | +| max | 138 | + +
+ Histogram (split: train, 40 documents) + +![slt_sciarg.png](img%2Fslt_sciarg.png) + +
+ +#### Token length (tokens) + +The token length is measured from the first token of the document to the last one. + +We collect the following statistics: number of documents in the split (*no. doc*), mean of document token-length (*mean*), standard deviation of the length (*std*), minimum number of tokens in a document (*min*), and maximum number of tokens in a document (*max*). +We also present histograms in the collasible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly. + +
+Command + +``` +python src/evaluate_documents.py dataset=sciarg_base metric=count_text_tokens +``` + +
+ +| statistics | train | +| :--------- | ------: | +| no. doc | 40 | +| mean | 10521.1 | +| std | 2472.2 | +| min | 6452 | +| max | 16421 | + +
+ Histogram (split: train, 40 documents) + +![tl_sciarg.png](img%2Ftl_sciarg.png) + +
## Dataset Creation diff --git a/AM_statistics/img/relation_token_distance_per_label/rtd-label_sciarg.png b/dataset_builders/pie/sciarg/img/rtd-label_sciarg.png similarity index 100% rename from AM_statistics/img/relation_token_distance_per_label/rtd-label_sciarg.png rename to dataset_builders/pie/sciarg/img/rtd-label_sciarg.png diff --git a/AM_statistics/img/span_len_token/slt_sciarg.png b/dataset_builders/pie/sciarg/img/slt_sciarg.png similarity index 100% rename from AM_statistics/img/span_len_token/slt_sciarg.png rename to dataset_builders/pie/sciarg/img/slt_sciarg.png diff --git a/AM_statistics/img/token_len/tl_sciarg.png b/dataset_builders/pie/sciarg/img/tl_sciarg.png similarity index 100% rename from AM_statistics/img/token_len/tl_sciarg.png rename to dataset_builders/pie/sciarg/img/tl_sciarg.png From d87bb8573bf121fa6602f45cc471053ec71bcf41 Mon Sep 17 00:00:00 2001 From: Ruangrin L <88072261+idalr@users.noreply.github.com> Date: Mon, 11 Mar 2024 16:46:23 +0100 Subject: [PATCH 30/42] deleted AM_statistics folder --- ...argument_outer_token_distance_per_label.md | 269 ------------------ AM_statistics/span_lengths_tokens.md | 213 -------------- AM_statistics/text_length_tokens.md | 207 -------------- 3 files changed, 689 deletions(-) delete mode 100644 AM_statistics/Relation_argument_outer_token_distance_per_label.md delete mode 100644 AM_statistics/span_lengths_tokens.md delete mode 100644 AM_statistics/text_length_tokens.md diff --git a/AM_statistics/Relation_argument_outer_token_distance_per_label.md b/AM_statistics/Relation_argument_outer_token_distance_per_label.md deleted file mode 100644 index 6ae7215b..00000000 --- a/AM_statistics/Relation_argument_outer_token_distance_per_label.md +++ /dev/null @@ -1,269 +0,0 @@ -# Relation argument (outer) token distance per label - -In this document, we collect statistics regarding the token distance between the argumentative units in a relation on our six argument-mining datasets. - -For the tokenization, we use `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased)) -to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). -The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance. - -We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). -We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. - -**Remark on statistics collection**: -To manually collect a statistics for each dataset, execute the command provided under each dataset, -using the base variant of the dataset configuration, namely, `*DATASET*_base`. -The script `evaluate_documents.py` comes from [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1). - -## AAE2 - -### train (322 documents) - -| | len | max | mean | min | std | -| :---------------- | ---: | --: | ------: | --: | ------: | -| ALL | 9002 | 514 | 102.582 | 9 | 93.76 | -| attacks | 810 | 442 | 127.622 | 10 | 109.283 | -| semantically_same | 552 | 514 | 301.638 | 25 | 73.756 | -| supports | 7640 | 493 | 85.545 | 9 | 74.023 | - -
- Histogram (split: train, 322 documents) - -![img_1.png](../dataset_builders/pie/aae2/img/rtd-label_aae2_train.png) - -
- -### test (80 documents) - -| | len | max | mean | min | std | -| :---------------- | ---: | --: | ------: | --: | -----: | -| ALL | 2372 | 442 | 100.711 | 10 | 92.698 | -| attacks | 184 | 402 | 115.891 | 12 | 98.751 | -| semantically_same | 146 | 442 | 299.671 | 34 | 72.921 | -| supports | 2042 | 437 | 85.118 | 10 | 75.023 | - -
- Histogram (split: test, 80 documents) - -![img.png](../dataset_builders/pie/aae2/img/rtd-label_aae2_test.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=aae2_base metric=count_relation_argument_distances -``` - -
- -## AbsRCT - -### neoplasm_train (350 documents) - -| | len | max | mean | min | std | -| :------------- | ---: | --: | ------: | --: | -----: | -| ALL | 2836 | 511 | 132.903 | 17 | 80.869 | -| Attack | 72 | 346 | 89.639 | 29 | 75.554 | -| Partial-Attack | 338 | 324 | 59.024 | 17 | 42.773 | -| Support | 2426 | 511 | 144.481 | 26 | 79.187 | - -
- Histogram (split: neoplasm_train, 350 documents) - -![img_2.png](img/relation_token_distance_per_label/rtd-label_abs-neo_train.png) - -
- -### neoplasm_dev (50 documents) - -| | len | max | mean | min | std | -| :------------- | --: | --: | ------: | --: | -----: | -| ALL | 438 | 625 | 146.393 | 24 | 98.788 | -| Attack | 16 | 200 | 90.375 | 26 | 62.628 | -| Partial-Attack | 50 | 240 | 72.04 | 24 | 47.685 | -| Support | 372 | 625 | 158.796 | 34 | 99.922 | - -
- Histogram (split: neoplasm_dev, 50 documents) - -![img_3.png](img/relation_token_distance_per_label/rtd-label_abs-neo_dev.png) - -
- -### neoplasm_test (100 documents) - -| | len | max | mean | min | std | -| :------------- | --: | --: | ------: | --: | -----: | -| ALL | 848 | 459 | 126.731 | 22 | 75.363 | -| Attack | 32 | 390 | 115.688 | 22 | 97.262 | -| Partial-Attack | 88 | 205 | 56.955 | 24 | 34.534 | -| Support | 728 | 459 | 135.651 | 33 | 73.365 | - -
- Histogram (split: neoplasm_test, 100 documents) - -![img_4.png](img/relation_token_distance_per_label/rtd-label_abs-neo_test.png) - -
- -### glaucoma_test (100 documents) - -| | len | max | mean | min | std | -| :------------- | --: | --: | ------: | --: | -----: | -| ALL | 734 | 488 | 159.166 | 26 | 83.885 | -| Attack | 14 | 177 | 89 | 47 | 40.171 | -| Partial-Attack | 52 | 259 | 74 | 26 | 51.239 | -| Support | 668 | 488 | 167.266 | 38 | 82.222 | - -
- Histogram (split: glaucoma_test, 100 documents) - -![img_5.png](img/relation_token_distance_per_label/rtd-label_abs-glu_test.png) - -
- -### mixed_test (100 documents) - -| | len | max | mean | min | std | -| :------------- | --: | --: | ------: | --: | ------: | -| ALL | 658 | 459 | 145.067 | 23 | 77.921 | -| Attack | 6 | 411 | 164 | 34 | 174.736 | -| Partial-Attack | 42 | 259 | 65.762 | 23 | 62.426 | -| Support | 610 | 459 | 150.341 | 35 | 74.273 | - -
- Histogram (split: mixed_test, 100 documents) - -![img_6.png](img/relation_token_distance_per_label/rtd-label_abs-mix_test.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=abstrct_base metric=count_relation_argument_distances -``` - -
- -## ArgMicro - -| | len | max | mean | min | std | -| :---- | ---: | --: | -----: | --: | -----: | -| ALL | 1018 | 127 | 44.434 | 14 | 21.501 | -| exa | 18 | 63 | 33.556 | 16 | 13.056 | -| joint | 88 | 48 | 30.091 | 17 | 9.075 | -| reb | 220 | 127 | 49.327 | 16 | 24.653 | -| sup | 562 | 124 | 46.534 | 14 | 22.079 | -| und | 130 | 84 | 38.292 | 17 | 12.321 | - -
- Histogram (split: train, 112 documents) - -![img_7.png](img/relation_token_distance_per_label/rtd-label_argmicro.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=argmicro_base metric=count_relation_argument_distances -``` - -
- -## CDCP - -### train (580 documents) - -| | len | max | mean | min | std | -| :------- | ---: | --: | -----: | --: | -----: | -| ALL | 2204 | 240 | 48.839 | 8 | 31.462 | -| evidence | 94 | 196 | 66.723 | 14 | 42.444 | -| reason | 2110 | 240 | 48.043 | 8 | 30.64 | - -
- Histogram (split: train, 580 documents) - -![img_8.png](../dataset_builders/pie/cdcp/img/rtd-label_cdcp_train.png) - -
- -### test (150 documents) - -| | len | max | mean | min | std | -| :------- | --: | --: | -----: | --: | -----: | -| ALL | 648 | 212 | 51.299 | 8 | 31.159 | -| evidence | 52 | 170 | 73.923 | 20 | 39.855 | -| reason | 596 | 212 | 49.326 | 8 | 29.47 | - -
- Histogram (split: test, 150 documents) - -![img_9.png](../dataset_builders/pie/cdcp/img/rtd-label_cdcp_test.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=cdcp_base metric=count_relation_argument_distances -``` - -
- -## SciArg - -| | len | max | mean | min | std | -| :---------------- | ----: | ---: | ------: | --: | ------: | -| ALL | 15640 | 2864 | 30.524 | 3 | 45.351 | -| contradicts | 1392 | 238 | 32.565 | 6 | 19.771 | -| parts_of_same | 2594 | 374 | 28.18 | 3 | 26.845 | -| semantically_same | 84 | 2864 | 206.333 | 11 | 492.268 | -| supports | 11570 | 407 | 29.527 | 4 | 24.189 | - -
- Histogram (split: train, 40 documents) - -![img_10.png](img/relation_token_distance_per_label/rtd-label_sciarg.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=sciarg_base metric=count_relation_argument_distances ++metric.tokenize_kwargs.strict_span_conversion=false -``` - -
- -## SciDTB_Argmin - -| | len | max | mean | min | std | -| :--------- | --: | --: | -----: | --: | -----: | -| ALL | 586 | 277 | 75.239 | 21 | 40.312 | -| additional | 54 | 180 | 59.593 | 36 | 29.306 | -| detail | 258 | 163 | 65.62 | 22 | 29.21 | -| sequence | 22 | 93 | 59.727 | 38 | 17.205 | -| support | 252 | 277 | 89.794 | 21 | 48.118 | - -
- Histogram (split: train, 60 documents) - -![img_11.png](../dataset_builders/pie/scidtb_argmin/img/rtd-label_scitdb-argmin.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=scidtb_argmin_base metric=count_relation_argument_distances -``` - -
diff --git a/AM_statistics/span_lengths_tokens.md b/AM_statistics/span_lengths_tokens.md deleted file mode 100644 index 0a1960b0..00000000 --- a/AM_statistics/span_lengths_tokens.md +++ /dev/null @@ -1,213 +0,0 @@ -# Span lengths (tokens) - -In this document, we collect statistics regarding the token distance between the argumentative units in a relation on our six argument-mining datasets. - -For the tokenization, we use `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased)) -to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). -The span length is measured from the first token of the first argumentative unit to the last token of the particular unit. - -We collect the following statistics: number of documents in the split (*no. doc*), no. of spans (*len*), mean of number of tokens in a span (*mean*), standard deviation of the number of tokens (*std*), minimum tokens in a span (*min*), and maximum tokens in a span (*max*). -We also present histograms in the collasible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly. - -**Remark on statistics collection**: -To manually collect a statistics for each dataset, execute the command provided under each dataset, -using the base variant of the dataset configuration, namely, `*DATASET*_base`. -The script `evaluate_documents.py` comes from [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1). - -## AAE2 - -| statistics | train | test | -| :--------- | -----: | -----: | -| no. doc | 322 | 80 | -| len | 4823 | 1266 | -| mean | 17.157 | 16.317 | -| std | 8.079 | 7.953 | -| min | 3 | 3 | -| max | 75 | 50 | - -
- Histogram (split: train, 322 documents) - -![slt_aae2_train.png](img%2Fspan_len_token%2Fslt_aae2_train.png) - -
-
- Histogram (split: test, 80 documents) - -![slt_aae2_test.png](img%2Fspan_len_token%2Fslt_aae2_test.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=aae2_base metric=span_lengths_tokens -``` - -
- -## AbsRCT - -| statistics | neoplasm_train | neoplasm_dev | neoplasm_test | glaucoma_test | mixed_test | -| :--------- | -------------: | -----------: | ------------: | ------------: | ---------: | -| no. doc | 350 | 50 | 100 | 100 | 100 | -| len | 2267 | 326 | 686 | 594 | 600 | -| mean | 34.303 | 37.135 | 32.566 | 38.997 | 38.507 | -| std | 22.425 | 29.941 | 20.264 | 22.604 | 24.036 | -| min | 5 | 5 | 6 | 6 | 7 | -| max | 250 | 288 | 182 | 169 | 159 | - -
- Histogram (split: neoplasm_train, 350 documents) - -![slt_abs-neo_train.png](img%2Fspan_len_token%2Fslt_abs-neo_train.png) - -
-
- Histogram (split: neoplasm_dev, 50 documents) - -![slt_abs-neo_dev.png](img%2Fspan_len_token%2Fslt_abs-neo_dev.png) - -
-
- Histogram (split: neoplasm_test, 100 documents) - -![slt_abs-neo_test.png](img%2Fspan_len_token%2Fslt_abs-neo_test.png) - -
-
- Histogram (split: glucoma_test, 100 documents) - -![slt_abs-glu_test.png](img%2Fspan_len_token%2Fslt_abs-glu_test.png) - -
-
- Histogram (split: mixed_test, 100 documents) - -![slt_abs-mix_test.png](img%2Fspan_len_token%2Fslt_abs-mix_test.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=abstrct_base metric=span_lengths_tokens -``` - -
- -## ArgMicro - -| statistics | train | -| :--------- | -----: | -| no. doc | 112 | -| len | 576 | -| mean | 16.365 | -| std | 6.545 | -| min | 4 | -| max | 41 | - -
- Histogram (split: train, 112 documents) - -![slt_argmicro.png](img%2Fspan_len_token%2Fslt_argmicro.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=argmicro_base metric=span_lengths_tokens -``` - -
- -## CDCP - -| statistics | train | test | -| :--------- | -----: | -----: | -| no. doc | 580 | 150 | -| len | 3901 | 1026 | -| mean | 19.441 | 18.758 | -| std | 11.71 | 10.388 | -| min | 2 | 3 | -| max | 142 | 83 | - -
- Histogram (split: train, 580 documents) - -![slt_cdcp_train.png](img%2Fspan_len_token%2Fslt_cdcp_train.png) - -
-
- Histogram (split: test, 150 documents) - -![slt_cdcp_test.png](img%2Fspan_len_token%2Fslt_cdcp_test.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=cdcp_base metric=span_lengths_tokens -``` - -
- -## SciArg - -| statistics | train | -| :--------- | -----: | -| no. doc | 40 | -| len | 13586 | -| mean | 11.677 | -| std | 8.731 | -| min | 1 | -| max | 138 | - -
- Histogram (split: train, 40 documents) - -![slt_sciarg.png](img%2Fspan_len_token%2Fslt_sciarg.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=sciarg_base metric=span_lengths_tokens ++metric.tokenize_kwargs.strict_span_conversion=false -``` - -
- -## SciDTB_Argmin - -| statistics | train | -| :--------- | -----: | -| no. doc | 60 | -| len | 353 | -| mean | 27.946 | -| std | 13.054 | -| min | 7 | -| max | 123 | - -
- Histogram (split: train, 60 documents) - -![slt_scitdb-argmin.png](img%2Fspan_len_token%2Fslt_scitdb-argmin.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=scidtb_argmin_base metric=span_lengths_tokens -``` - -
diff --git a/AM_statistics/text_length_tokens.md b/AM_statistics/text_length_tokens.md deleted file mode 100644 index c951fb3b..00000000 --- a/AM_statistics/text_length_tokens.md +++ /dev/null @@ -1,207 +0,0 @@ -# Text length (tokens) - -In this document, we collect statistics regarding the token distance between the argumentative units in a relation on our six argument-mining datasets. - -For the tokenization, we use `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased)) -to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). -The text (document) length is measured from the first token of the document to the last one. - -We collect the following statistics: number of documents in the split (*no. doc*), mean of document token-length (*mean*), standard deviation of the length (*std*), minimum number of tokens in a document (*min*), and maximum number of tokens in a document (*max*). -We also present histograms in the collasible, showing the distribution of these text lengths (x-axis; and unit-counts in y-axis), accordingly. - -**Remark on statistics collection**: -To manually collect a statistics for each dataset, execute the command provided under each dataset, -using the base variant of the dataset configuration, namely, `*DATASET*_base`. -The script `evaluate_documents.py` comes from [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1). - -## AAE2 - -| statistics | train | test | -| :--------- | ------: | -----: | -| no. doc | 322 | 80 | -| mean | 377.686 | 378.4 | -| std | 64.534 | 66.054 | -| min | 236 | 269 | -| max | 580 | 532 | - -
- Histogram (split: train, 322 documents) - -![tl_aae2_train.png](img%2Ftoken_len%2Ftl_aae2_train.png) - -
-
- Histogram (split: test, 80 documents) - -![tl_aae2_test.png](img%2Ftoken_len%2Ftl_aae2_test.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=aae2_base metric=count_text_tokens -``` - -
- -## AbsRCT - -| statistics | neoplasm_train | neoplasm_dev | neoplasm_test | glaucoma_test | mixed_test | -| :--------- | -------------: | -----------: | ------------: | ------------: | ---------: | -| no. doc | 350 | 50 | 100 | 100 | 100 | -| mean | 447.291 | 481.66 | 442.79 | 456.78 | 450.29 | -| std | 91.266 | 116.239 | 89.692 | 115.535 | 87.002 | -| min | 301 | 329 | 292 | 212 | 268 | -| max | 843 | 952 | 776 | 1022 | 776 | - -
- Histogram (split: neoplasm_train, 350 documents) - -![tl_abs-neo_train.png](img%2Ftoken_len%2Ftl_abs-neo_train.png) - -
-
- Histogram (split: neoplasm_dev, 50 documents) - -![tl_abs-neo_dev.png](img%2Ftoken_len%2Ftl_abs-neo_dev.png) - -
-
- Histogram (split: neoplasm_test, 100 documents) - -![tl_abs-neo_test.png](img%2Ftoken_len%2Ftl_abs-neo_test.png) - -
-
- Histogram (split: glucoma_test, 100 documents) - -![tl_abs-glu_test.png](img%2Ftoken_len%2Ftl_abs-glu_test.png) - -
-
- Histogram (split: mixed_test, 100 documents) - -![tl_abs-mix_test.png](img%2Ftoken_len%2Ftl_abs-mix_test.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=abstrct_base metric=count_text_tokens -``` - -
- -## ArgMicro - -| statistics | train | -| :--------- | -----: | -| no. doc | 112 | -| mean | 84.161 | -| std | 22.596 | -| min | 36 | -| max | 153 | - -
- Histogram (split: train, 112 documents) - -![tl_argmicro.png](img%2Ftoken_len%2Ftl_argmicro.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=argmicro_base metric=count_text_tokens -``` - -
- -## CDCP - -| statistics | train | test | -| :--------- | ------: | ------: | -| no. doc | 580 | 150 | -| mean | 130.781 | 128.673 | -| std | 101.121 | 98.708 | -| min | 13 | 15 | -| max | 562 | 571 | - -
- Histogram (split: train, 580 documents) - -![tl_cdcp_train.png](img%2Ftoken_len%2Ftl_cdcp_train.png) - -
-
- Histogram (split: test, 150 documents) - -![tl_cdcp_test.png](img%2Ftoken_len%2Ftl_cdcp_test.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=cdcp_base metric=count_text_tokens -``` - -
- -## SciArg - -| statistics | train | -| :--------- | ------: | -| no. doc | 40 | -| mean | 10521.1 | -| std | 2472.2 | -| min | 6452 | -| max | 16421 | - -
- Histogram (split: train, 40 documents) - -![tl_sciarg.png](img%2Ftoken_len%2Ftl_sciarg.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=sciarg_base metric=count_text_tokens -``` - -
- -## SciDTB_Argmin - -| statistics | train | -| :--------- | ------: | -| no. doc | 60 | -| mean | 164.417 | -| std | 64.572 | -| min | 80 | -| max | 532 | - -
- Histogram (split: train, 60 documents) - -![tl_scidtb-argmin.png](img%2Ftoken_len%2Ftl_scidtb-argmin.png) - -
- -
-Command - -``` -python src/evaluate_documents.py dataset=scidtb_argmin_base metric=count_text_tokens -``` - -
From c55db077f6263accc171889140ae306090e33c3e Mon Sep 17 00:00:00 2001 From: Ruangrin L <88072261+idalr@users.noreply.github.com> Date: Mon, 11 Mar 2024 16:53:50 +0100 Subject: [PATCH 31/42] minor edit --- dataset_builders/pie/argmicro/README.md | 2 -- dataset_builders/pie/cdcp/README.md | 2 -- dataset_builders/pie/scidtb_argmin/README.md | 2 -- 3 files changed, 6 deletions(-) diff --git a/dataset_builders/pie/argmicro/README.md b/dataset_builders/pie/argmicro/README.md index b1b281a0..d98944ee 100644 --- a/dataset_builders/pie/argmicro/README.md +++ b/dataset_builders/pie/argmicro/README.md @@ -74,8 +74,6 @@ input: path: pie/argmicro revision: 28ef031d2a2c97be7e9ed360e1a5b20bd55b57b2 name: en - base_dataset_kwargs: - data_dir: data/datasets/arg-microtexts-master.zip ``` For token based metrics, this uses `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased) to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). diff --git a/dataset_builders/pie/cdcp/README.md b/dataset_builders/pie/cdcp/README.md index c90e968c..b250c86a 100644 --- a/dataset_builders/pie/cdcp/README.md +++ b/dataset_builders/pie/cdcp/README.md @@ -52,8 +52,6 @@ input: _target_: pie_datasets.DatasetDict.load_dataset path: pie/cdcp revision: 001722894bdca6df6a472d0d186a3af103e392c5 - base_dataset_kwargs: - data_dir: data/datasets/cdcp_acl17.zip ``` For token based metrics, this uses `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased) to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). diff --git a/dataset_builders/pie/scidtb_argmin/README.md b/dataset_builders/pie/scidtb_argmin/README.md index d50718d8..50a42a29 100644 --- a/dataset_builders/pie/scidtb_argmin/README.md +++ b/dataset_builders/pie/scidtb_argmin/README.md @@ -46,8 +46,6 @@ input: _target_: pie_datasets.DatasetDict.load_dataset path: pie/scidtb_argmin revision: 335a8e6168919d7f204c6920eceb96745dbd161b - base_dataset_kwargs: - data_dir: data/datasets/scidtb_argmin_annotations.tgz ``` For token based metrics, this uses `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased) to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)). From 2d04194968f6bc3bda0ada05b7280dbe31229daa Mon Sep 17 00:00:00 2001 From: Ruangrin L <88072261+idalr@users.noreply.github.com> Date: Mon, 11 Mar 2024 17:04:41 +0100 Subject: [PATCH 32/42] fixed typos --- dataset_builders/pie/aae2/README.md | 6 +++--- dataset_builders/pie/abstrct/README.md | 6 +++--- dataset_builders/pie/argmicro/README.md | 6 +++--- dataset_builders/pie/cdcp/README.md | 6 +++--- dataset_builders/pie/sciarg/README.md | 6 +++--- dataset_builders/pie/scidtb_argmin/README.md | 6 +++--- 6 files changed, 18 insertions(+), 18 deletions(-) diff --git a/dataset_builders/pie/aae2/README.md b/dataset_builders/pie/aae2/README.md index 434cd4d3..a0e4152e 100644 --- a/dataset_builders/pie/aae2/README.md +++ b/dataset_builders/pie/aae2/README.md @@ -158,7 +158,7 @@ For relation-label statistics, we collect those from the default relation conver The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance. We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). -We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly.
Command @@ -206,7 +206,7 @@ python src/evaluate_documents.py dataset=aae2_base metric=relation_argument_toke The span length is measured from the first token of the first argumentative unit to the last token of the particular unit. We collect the following statistics: number of documents in the split (*no. doc*), no. of spans (*len*), mean of number of tokens in a span (*mean*), standard deviation of the number of tokens (*std*), minimum tokens in a span (*min*), and maximum tokens in a span (*max*). -We also present histograms in the collasible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly.
Command @@ -244,7 +244,7 @@ python src/evaluate_documents.py dataset=aae2_base metric=span_lengths_tokens The token length is measured from the first token of the document to the last one. We collect the following statistics: number of documents in the split (*no. doc*), mean of document token-length (*mean*), standard deviation of the length (*std*), minimum number of tokens in a document (*min*), and maximum number of tokens in a document (*max*). -We also present histograms in the collasible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly.
Command diff --git a/dataset_builders/pie/abstrct/README.md b/dataset_builders/pie/abstrct/README.md index 95487edc..64c6cfef 100644 --- a/dataset_builders/pie/abstrct/README.md +++ b/dataset_builders/pie/abstrct/README.md @@ -137,7 +137,7 @@ For token based metrics, this uses `bert-base-uncased` from `transformer.AutoTok The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance. We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). -We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly.
Command @@ -233,7 +233,7 @@ python src/evaluate_documents.py dataset=abstrct_base metric=relation_argument_t The span length is measured from the first token of the first argumentative unit to the last token of the particular unit. We collect the following statistics: number of documents in the split (*no. doc*), no. of spans (*len*), mean of number of tokens in a span (*mean*), standard deviation of the number of tokens (*std*), minimum tokens in a span (*min*), and maximum tokens in a span (*max*). -We also present histograms in the collasible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly.
Command @@ -289,7 +289,7 @@ python src/evaluate_documents.py dataset=abstrct_base metric=span_lengths_tokens The token length is measured from the first token of the document to the last one. We collect the following statistics: number of documents in the split (*no. doc*), mean of document token-length (*mean*), standard deviation of the length (*std*), minimum number of tokens in a document (*min*), and maximum number of tokens in a document (*max*). -We also present histograms in the collasible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly.
Command diff --git a/dataset_builders/pie/argmicro/README.md b/dataset_builders/pie/argmicro/README.md index d98944ee..67936d8d 100644 --- a/dataset_builders/pie/argmicro/README.md +++ b/dataset_builders/pie/argmicro/README.md @@ -83,7 +83,7 @@ For token based metrics, this uses `bert-base-uncased` from `transformer.AutoTok The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance. We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). -We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly.
Command @@ -115,7 +115,7 @@ python src/evaluate_documents.py dataset=argmicro_base metric=relation_argument_ The span length is measured from the first token of the first argumentative unit to the last token of the particular unit. We collect the following statistics: number of documents in the split (*no. doc*), no. of spans (*len*), mean of number of tokens in a span (*mean*), standard deviation of the number of tokens (*std*), minimum tokens in a span (*min*), and maximum tokens in a span (*max*). -We also present histograms in the collasible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly.
Command @@ -147,7 +147,7 @@ python src/evaluate_documents.py dataset=argmicro_base metric=span_lengths_token The token length is measured from the first token of the document to the last one. We collect the following statistics: number of documents in the split (*no. doc*), mean of document token-length (*mean*), standard deviation of the length (*std*), minimum number of tokens in a document (*min*), and maximum number of tokens in a document (*max*). -We also present histograms in the collasible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly.
Command diff --git a/dataset_builders/pie/cdcp/README.md b/dataset_builders/pie/cdcp/README.md index b250c86a..0ff5ecee 100644 --- a/dataset_builders/pie/cdcp/README.md +++ b/dataset_builders/pie/cdcp/README.md @@ -61,7 +61,7 @@ For token based metrics, this uses `bert-base-uncased` from `transformer.AutoTok The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance. We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). -We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly.
Command @@ -107,7 +107,7 @@ python src/evaluate_documents.py dataset=cdcp_base metric=relation_argument_toke The span length is measured from the first token of the first argumentative unit to the last token of the particular unit. We collect the following statistics: number of documents in the split (*no. doc*), no. of spans (*len*), mean of number of tokens in a span (*mean*), standard deviation of the number of tokens (*std*), minimum tokens in a span (*min*), and maximum tokens in a span (*max*). -We also present histograms in the collasible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly.
Command @@ -145,7 +145,7 @@ python src/evaluate_documents.py dataset=cdcp_base metric=span_lengths_tokens The token length is measured from the first token of the document to the last one. We collect the following statistics: number of documents in the split (*no. doc*), mean of document token-length (*mean*), standard deviation of the length (*std*), minimum number of tokens in a document (*min*), and maximum number of tokens in a document (*max*). -We also present histograms in the collasible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly.
Command diff --git a/dataset_builders/pie/sciarg/README.md b/dataset_builders/pie/sciarg/README.md index c6e90b45..ee5bb318 100644 --- a/dataset_builders/pie/sciarg/README.md +++ b/dataset_builders/pie/sciarg/README.md @@ -227,7 +227,7 @@ For token based metrics, this uses `bert-base-uncased` from `transformer.AutoTok The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance. We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). -We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly.
Command @@ -258,7 +258,7 @@ python src/evaluate_documents.py dataset=sciarg_base metric=relation_argument_to The span length is measured from the first token of the first argumentative unit to the last token of the particular unit. We collect the following statistics: number of documents in the split (*no. doc*), no. of spans (*len*), mean of number of tokens in a span (*mean*), standard deviation of the number of tokens (*std*), minimum tokens in a span (*min*), and maximum tokens in a span (*max*). -We also present histograms in the collasible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly.
Command @@ -290,7 +290,7 @@ python src/evaluate_documents.py dataset=sciarg_base metric=span_lengths_tokens The token length is measured from the first token of the document to the last one. We collect the following statistics: number of documents in the split (*no. doc*), mean of document token-length (*mean*), standard deviation of the length (*std*), minimum number of tokens in a document (*min*), and maximum number of tokens in a document (*max*). -We also present histograms in the collasible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly.
Command diff --git a/dataset_builders/pie/scidtb_argmin/README.md b/dataset_builders/pie/scidtb_argmin/README.md index 50a42a29..21f05419 100644 --- a/dataset_builders/pie/scidtb_argmin/README.md +++ b/dataset_builders/pie/scidtb_argmin/README.md @@ -55,7 +55,7 @@ For token based metrics, this uses `bert-base-uncased` from `transformer.AutoTok The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance. We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*). -We also present histograms in the collasible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly.
Command @@ -86,7 +86,7 @@ python src/evaluate_documents.py dataset=scidtb_argmin_base metric=relation_argu The span length is measured from the first token of the first argumentative unit to the last token of the particular unit. We collect the following statistics: number of documents in the split (*no. doc*), no. of spans (*len*), mean of number of tokens in a span (*mean*), standard deviation of the number of tokens (*std*), minimum tokens in a span (*min*), and maximum tokens in a span (*max*). -We also present histograms in the collasible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly.
Command @@ -118,7 +118,7 @@ python src/evaluate_documents.py dataset=scidtb_argmin_base metric=span_lengths_ The token length is measured from the first token of the document to the last one. We collect the following statistics: number of documents in the split (*no. doc*), mean of document token-length (*mean*), standard deviation of the length (*std*), minimum number of tokens in a document (*min*), and maximum number of tokens in a document (*max*). -We also present histograms in the collasible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly. +We also present histograms in the collapsible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly.
Command From 768eecf97de45c3e128c27e57c6b40b90ebe9d56 Mon Sep 17 00:00:00 2001 From: Arne Binder Date: Thu, 7 Nov 2024 13:09:57 +0100 Subject: [PATCH 33/42] minor adjustments --- dataset_builders/pie/aae2/README.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/dataset_builders/pie/aae2/README.md b/dataset_builders/pie/aae2/README.md index a0e4152e..6d6d4378 100644 --- a/dataset_builders/pie/aae2/README.md +++ b/dataset_builders/pie/aae2/README.md @@ -8,7 +8,8 @@ Therefore, the `aae2` dataset as described here follows the data structure from Argument Annotated Essays Corpus (AAEC) ([Stab and Gurevych, 2017](https://aclanthology.org/J17-3005.pdf)) contains student essays. A stance for a controversial theme is expressed by a major claim component as well as claim components, and premise components justify or refute the claims. Attack and support labels are defined as relations. The span covers a statement, *which can stand in isolation as a complete sentence*, according to the AAEC annotation guidelines. All components are annotated with minimum boundaries of a clause or sentence excluding so-called "shell" language such as *On the other hand* and *Hence*. (Morio et al., 2022, p. 642) -There is no premise that links to another premise or claim in a different paragraph. That means, an argumentation tree structure is complete within each paragraph. Therefore, it is possible to train a model on the full documents or just at the paragraph-level which is usually less memory-exhaustive (Eger et al., 2017, p. 16). +In the original dataset, there is no premise that links to another premise or claim in a different paragraph. That means, an argumentation tree structure is complete within each paragraph. Therefore, it is possible to train a model on the full documents or just at the paragraph-level which is usually less memory-exhaustive (Eger et al., 2017, p. 16). +However, through our `DOCUMENT_CONVERTERS`, we build links between claims, creating a graph structure throughout an entire essay (see [Document Converters](#document-converters)). ### Supported Tasks and Leaderboards @@ -50,7 +51,7 @@ assert isinstance(doc, builders.brat.BratDocumentWithMergedSpans) See further statistics in Stab & Gurevych (2017), p. 650, Table A.1. -### Label Descriptions +### Label Descriptions and Statistics #### Components @@ -64,8 +65,6 @@ See further statistics in Stab & Gurevych (2017), p. 650, Table A.1. - `Claim` constitutes the central component of each argument. Each one has at least one premise and takes stance attribute values "for" or "against" with regarding the major claim. - `Premise` is the reasons of the argument; either linked to claim or another premise. -**Note that** relations between `MajorClaim` and `Claim` were not annotated; however, each claim is annotated with an `Attribute` annotation with value `for` or `against` - which indicates the relation between itself and `MajorClaim`. In addition, when two non-related `Claim` 's appear in one paragraph, there is also no relations to one another. - #### Relations | Relations | Count | Percentage | @@ -79,6 +78,8 @@ See further statistics in Stab & Gurevych (2017), p. 650, Table A.1. See further description in Stab & Gurevych 2017, p.627 and the [annotation guideline](https://github.com/ArneBinder/pie-datasets/blob/db94035602610cefca2b1678aa2fe4455c96155d/data/datasets/ArgumentAnnotatedEssays-2.0/guideline.pdf). +**Note that** relations between `MajorClaim` and `Claim` were not annotated; however, each claim is annotated with an `Attribute` annotation with value `for` or `against` - which indicates the relation between itself and `MajorClaim`. In addition, when two non-related `Claim` 's appear in one paragraph, there is also no relations to one another. An example of a document is shown here below. + #### Example ![Example](img/sg17f2.png) From 10f21bd4cd7d516a26c48bfc861c40c2a49b148b Mon Sep 17 00:00:00 2001 From: Arne Binder Date: Thu, 7 Nov 2024 13:11:46 +0100 Subject: [PATCH 34/42] minor adjustments --- dataset_builders/pie/abstrct/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dataset_builders/pie/abstrct/README.md b/dataset_builders/pie/abstrct/README.md index 64c6cfef..66ea0d94 100644 --- a/dataset_builders/pie/abstrct/README.md +++ b/dataset_builders/pie/abstrct/README.md @@ -65,7 +65,7 @@ definitions. - `mixed_test` contains 20 abstracts on the following diseases: glaucoma, neoplasm, diabetes, hypertension, hepatitis. - 31 out of 40 abstracts in `mixed_test` overlap with abstracts in `neoplasm_test` and `glaucoma_test`. -### Label Descriptions +### Label Descriptions and Statistics In this section, we describe labels according to [Mayer et al. (2020)](https://ebooks.iospress.nl/publication/55129), as well as our label counts on 669 abstracts. From 27f92156d16f8eee9e56a9069e86296f1dfa22a2 Mon Sep 17 00:00:00 2001 From: Arne Binder Date: Thu, 7 Nov 2024 13:42:28 +0100 Subject: [PATCH 35/42] improve usage examples --- dataset_builders/pie/aae2/README.md | 34 +++++++++++++++-------- dataset_builders/pie/abstrct/README.md | 37 +++++++++++++++++--------- 2 files changed, 47 insertions(+), 24 deletions(-) diff --git a/dataset_builders/pie/aae2/README.md b/dataset_builders/pie/aae2/README.md index 6d6d4378..81c958a9 100644 --- a/dataset_builders/pie/aae2/README.md +++ b/dataset_builders/pie/aae2/README.md @@ -4,6 +4,29 @@ This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for t Therefore, the `aae2` dataset as described here follows the data structure from the [PIE brat dataset card](https://huggingface.co/datasets/pie/brat). +### Usage + +```python +from pie_datasets import load_dataset +from pie_datasets.builders.brat import BratDocumentWithMergedSpans +from pytorch_ie.documents import TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions + +# load default version +dataset = load_dataset("pie/aae2") +assert isinstance(dataset["train"][0], BratDocumentWithMergedSpans) + +# if required, normalize the document type (see section Document Converters below) +dataset_converted = dataset.to_document_type(TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions) +assert isinstance(dataset_converted["train"][0], TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions) + +# get first relation in the first document +doc = dataset_converted["train"][0] +print(doc.binary_relations[0]) +# BinaryRelation(head=LabeledSpan(start=716, end=851, label='Premise', score=1.0), tail=LabeledSpan(start=591, end=714, label='Claim', score=1.0), label='supports', score=1.0) +print(doc.binary_relations[0].resolve()) +# ('supports', (('Premise', 'What we acquired from team work is not only how to achieve the same goal with others but more importantly, how to get along with others'), ('Claim', 'through cooperation, children can learn about interpersonal skills which are significant in the future life of all students'))) +``` + ### Dataset Summary Argument Annotated Essays Corpus (AAEC) ([Stab and Gurevych, 2017](https://aclanthology.org/J17-3005.pdf)) contains student essays. A stance for a controversial theme is expressed by a major claim component as well as claim components, and premise components justify or refute the claims. Attack and support labels are defined as relations. The span covers a statement, *which can stand in isolation as a complete sentence*, according to the AAEC annotation guidelines. All components are annotated with minimum boundaries of a clause or sentence excluding so-called "shell" language such as *On the other hand* and *Hence*. (Morio et al., 2022, p. 642) @@ -28,17 +51,6 @@ The `aae2` dataset comes in a single version (`default`) with `BratDocumentWithM See [PIE-Brat Data Schema](https://huggingface.co/datasets/pie/brat#data-schema). -### Usage - -```python -from pie_datasets import load_dataset, builders - -# load default version -datasets = load_dataset("pie/aae2") -doc = datasets["train"][0] -assert isinstance(doc, builders.brat.BratDocumentWithMergedSpans) -``` - ### Data Splits | Statistics | Train | Test | diff --git a/dataset_builders/pie/abstrct/README.md b/dataset_builders/pie/abstrct/README.md index 66ea0d94..0bbf2daa 100644 --- a/dataset_builders/pie/abstrct/README.md +++ b/dataset_builders/pie/abstrct/README.md @@ -10,6 +10,29 @@ A novel corpus of healthcare texts (i.e., RCT abstracts on various diseases) fro are annotated with argumentative components (i.e., `MajorClaim`, `Claim`, and `Premise`) and relations (i.e., `Support`, `Attack`, and `Partial-attack`), in order to support clinicians' daily tasks in information finding and evidence-based reasoning for decision making. +### Usage + +```python +from pie_datasets import load_dataset +from pie_datasets.builders.brat import BratDocumentWithMergedSpans +from pytorch_ie.documents import TextDocumentWithLabeledSpansAndBinaryRelations + +# load default version +dataset = load_dataset("pie/abstrct") +assert isinstance(dataset["neoplasm_train"][0], BratDocumentWithMergedSpans) + +# if required, normalize the document type (see section Document Converters below) +dataset_converted = dataset.to_document_type("pytorch_ie.documents.TextDocumentWithLabeledSpansAndBinaryRelations") +assert isinstance(dataset_converted["neoplasm_train"][0], TextDocumentWithLabeledSpansAndBinaryRelations) + +# get first relation in the first document +doc = dataset_converted["neoplasm_train"][0] +print(doc.binary_relations[0]) +# BinaryRelation(head=LabeledSpan(start=1769, end=1945, label='Claim', score=1.0), tail=LabeledSpan(start=1, end=162, label='MajorClaim', score=1.0), label='Support', score=1.0) +print(doc.binary_relations[0].resolve()) +# ('Support', (('Claim', 'Treatment with mitoxantrone plus prednisone was associated with greater and longer-lasting improvement in several HQL domains and symptoms than treatment with prednisone alone.'), ('MajorClaim', 'A combination of mitoxantrone plus prednisone is preferable to prednisone alone for reduction of pain in men with metastatic, hormone-resistant, prostate cancer.'))) +``` + ### Supported Tasks and Leaderboards - **Tasks**: Argumentation Mining, Component Identification, Boundary Detection, Relation Identification, Link Prediction @@ -30,17 +53,6 @@ Without any need to merge fragments, the document type `BratDocumentWithMergedSp See [PIE-Brat Data Schema](https://huggingface.co/datasets/pie/brat#data-schema). -### Usage - -```python -from pie_datasets import load_dataset, builders - -# load default version -datasets = load_dataset("pie/abstrct") -doc = datasets["neoplasm_train"][0] -assert isinstance(doc, builders.brat.BratDocumentWithMergedSpans) -``` - ### Document Converters The dataset provides document converters for the following target document types: @@ -51,8 +63,7 @@ The dataset provides document converters for the following target document types - `BinraryRelations`, converted from `BratDocumentWithMergedSpans`'s `relations` - labels: `Support`, `Partial-Attack`, `Attack` -See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py) for the document type -definitions. +See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py) for the document type definitions. ### Data Splits From 19ecf0266c41d2c3eefa4a3d04a6eef2e4ee4853 Mon Sep 17 00:00:00 2001 From: Arne Binder Date: Thu, 7 Nov 2024 13:46:16 +0100 Subject: [PATCH 36/42] add usage example to argmicro --- dataset_builders/pie/argmicro/README.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/dataset_builders/pie/argmicro/README.md b/dataset_builders/pie/argmicro/README.md index 67936d8d..95827afc 100644 --- a/dataset_builders/pie/argmicro/README.md +++ b/dataset_builders/pie/argmicro/README.md @@ -3,6 +3,27 @@ This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for the [ArgMicro Huggingface dataset loading script](https://huggingface.co/datasets/DFKI-SLT/argmicro). +## Usage + +```python +from pie_datasets import load_dataset +from pytorch_ie.documents import TextDocumentWithLabeledSpansAndBinaryRelations + +# load English variant +dataset = load_dataset("pie/argmicro", name="en") + +# if required, normalize the document type (see section Document Converters below) +dataset_converted = dataset.to_document_type("pytorch_ie.documents.TextDocumentWithLabeledSpansAndBinaryRelations") +assert isinstance(dataset_converted["train"][0], TextDocumentWithLabeledSpansAndBinaryRelations) + +# get first relation in the first document +doc = dataset_converted["train"][0] +print(doc.binary_relations[0]) +# BinaryRelation(head=LabeledSpan(start=1769, end=1945, label='Claim', score=1.0), tail=LabeledSpan(start=1, end=162, label='MajorClaim', score=1.0), label='Support', score=1.0) +print(doc.binary_relations[0].resolve()) +# ('Support', (('Claim', 'Treatment with mitoxantrone plus prednisone was associated with greater and longer-lasting improvement in several HQL domains and symptoms than treatment with prednisone alone.'), ('MajorClaim', 'A combination of mitoxantrone plus prednisone is preferable to prednisone alone for reduction of pain in men with metastatic, hormone-resistant, prostate cancer.'))) +``` + ## Dataset Variants The dataset contains two `BuilderConfig`'s: From 5d0c88b61b8c9aa878b336e2432387bb7b73bb9b Mon Sep 17 00:00:00 2001 From: Arne Binder Date: Thu, 7 Nov 2024 13:51:25 +0100 Subject: [PATCH 37/42] fix argmicro usage example --- dataset_builders/pie/argmicro/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/dataset_builders/pie/argmicro/README.md b/dataset_builders/pie/argmicro/README.md index 95827afc..9de51471 100644 --- a/dataset_builders/pie/argmicro/README.md +++ b/dataset_builders/pie/argmicro/README.md @@ -13,15 +13,15 @@ from pytorch_ie.documents import TextDocumentWithLabeledSpansAndBinaryRelations dataset = load_dataset("pie/argmicro", name="en") # if required, normalize the document type (see section Document Converters below) -dataset_converted = dataset.to_document_type("pytorch_ie.documents.TextDocumentWithLabeledSpansAndBinaryRelations") +dataset_converted = dataset.to_document_type(TextDocumentWithLabeledSpansAndBinaryRelations) assert isinstance(dataset_converted["train"][0], TextDocumentWithLabeledSpansAndBinaryRelations) # get first relation in the first document doc = dataset_converted["train"][0] print(doc.binary_relations[0]) -# BinaryRelation(head=LabeledSpan(start=1769, end=1945, label='Claim', score=1.0), tail=LabeledSpan(start=1, end=162, label='MajorClaim', score=1.0), label='Support', score=1.0) +# BinaryRelation(head=LabeledSpan(start=0, end=81, label='opp', score=1.0), tail=LabeledSpan(start=326, end=402, label='pro', score=1.0), label='reb', score=1.0) print(doc.binary_relations[0].resolve()) -# ('Support', (('Claim', 'Treatment with mitoxantrone plus prednisone was associated with greater and longer-lasting improvement in several HQL domains and symptoms than treatment with prednisone alone.'), ('MajorClaim', 'A combination of mitoxantrone plus prednisone is preferable to prednisone alone for reduction of pain in men with metastatic, hormone-resistant, prostate cancer.'))) +# ('reb', (('opp', "Yes, it's annoying and cumbersome to separate your rubbish properly all the time."), ('pro', 'We Berliners should take the chance and become pioneers in waste separation!'))) ``` ## Dataset Variants From d5157cbc4ffb0371d1b89ff8dd3693ecaf76c41b Mon Sep 17 00:00:00 2001 From: Arne Binder Date: Thu, 7 Nov 2024 13:51:46 +0100 Subject: [PATCH 38/42] add cdcp usage example --- dataset_builders/pie/cdcp/README.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/dataset_builders/pie/cdcp/README.md b/dataset_builders/pie/cdcp/README.md index 0ff5ecee..1ea8748c 100644 --- a/dataset_builders/pie/cdcp/README.md +++ b/dataset_builders/pie/cdcp/README.md @@ -3,6 +3,27 @@ This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for the [CDCP Huggingface dataset loading script](https://huggingface.co/datasets/DFKI-SLT/cdcp). +## Usage + +```python +from pie_datasets import load_dataset +from pytorch_ie.documents import TextDocumentWithLabeledSpansAndBinaryRelations + +# load English variant +dataset = load_dataset("pie/cdcp") + +# if required, normalize the document type (see section Document Converters below) +dataset_converted = dataset.to_document_type(TextDocumentWithLabeledSpansAndBinaryRelations) +assert isinstance(dataset_converted["train"][0], TextDocumentWithLabeledSpansAndBinaryRelations) + +# get first relation in the first document +doc = dataset_converted["train"][0] +print(doc.binary_relations[0]) +# BinaryRelation(head=LabeledSpan(start=0, end=78, label='value', score=1.0), tail=LabeledSpan(start=79, end=242, label='value', score=1.0), label='reason', score=1.0) +print(doc.binary_relations[0].resolve()) +# ('reason', (('value', 'State and local court rules sometimes make default judgments much more likely.'), ('value', 'For example, when a person who allegedly owes a debt is told to come to court on a work day, they may be forced to choose between a default judgment and their job.'))) +``` + ## Data Schema The document type for this dataset is `CDCPDocument` which defines the following data fields: From e72f2d4f6bd649b5473085c01ac5cfa5cc1e2edc Mon Sep 17 00:00:00 2001 From: Arne Binder Date: Thu, 7 Nov 2024 13:54:14 +0100 Subject: [PATCH 39/42] add usage example to scidtb_argmin minor and fix --- dataset_builders/pie/scidtb_argmin/README.md | 23 +++++++++++++++++++- 1 file changed, 22 insertions(+), 1 deletion(-) diff --git a/dataset_builders/pie/scidtb_argmin/README.md b/dataset_builders/pie/scidtb_argmin/README.md index 21f05419..eb7d2b43 100644 --- a/dataset_builders/pie/scidtb_argmin/README.md +++ b/dataset_builders/pie/scidtb_argmin/README.md @@ -3,11 +3,32 @@ This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for the [SciDTB ArgMin Huggingface dataset loading script](https://huggingface.co/datasets/DFKI-SLT/scidtb_argmin). +## Usage + +```python +from pie_datasets import load_dataset +from pytorch_ie.documents import TextDocumentWithLabeledSpansAndBinaryRelations + +# load English variant +dataset = load_dataset("pie/scidtb_argmin") + +# if required, normalize the document type (see section Document Converters below) +dataset_converted = dataset.to_document_type(TextDocumentWithLabeledSpansAndBinaryRelations) +assert isinstance(dataset_converted["train"][0], TextDocumentWithLabeledSpansAndBinaryRelations) + +# get first relation in the first document +doc = dataset_converted["train"][0] +print(doc.binary_relations[0]) +# BinaryRelation(head=LabeledSpan(start=251, end=454, label='means', score=1.0), tail=LabeledSpan(start=455, end=712, label='proposal', score=1.0), label='detail', score=1.0) +print(doc.binary_relations[0].resolve()) +# ('detail', (('means', 'We observe , identify , and detect naturally occurring signals of interestingness in click transitions on the Web between source and target documents , which we collect from commercial Web browser logs .'), ('proposal', 'The DSSM is trained on millions of Web transitions , and maps source-target document pairs to feature vectors in a latent space in such a way that the distance between source documents and their corresponding interesting targets in that space is minimized .'))) +``` + ## Data Schema The document type for this dataset is `SciDTBArgminDocument` which defines the following data fields: -- `tokens` (Tuple of string) +- `tokens` (tuple of string) - `id` (str, optional) - `metadata` (dictionary, optional) From 045496d8e8c88f51f721613c0ab5ed8ec30d24a0 Mon Sep 17 00:00:00 2001 From: Arne Binder Date: Thu, 7 Nov 2024 13:54:38 +0100 Subject: [PATCH 40/42] add details to document converter --- dataset_builders/pie/scidtb_argmin/README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/dataset_builders/pie/scidtb_argmin/README.md b/dataset_builders/pie/scidtb_argmin/README.md index eb7d2b43..cb577a6b 100644 --- a/dataset_builders/pie/scidtb_argmin/README.md +++ b/dataset_builders/pie/scidtb_argmin/README.md @@ -44,6 +44,11 @@ See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/a The dataset provides document converters for the following target document types: - `pytorch_ie.documents.TextDocumentWithLabeledSpansAndBinaryRelations` + - `labeled_spans`: `LabeledSpan` annotations, converted from`SciDTBArgminDocument`'s `units` + - labels: `proposal`, `assertion`, `result`, `observation`, `means`, `description` + - tuples of `tokens` are joined with a whitespace to create `text` for `LabeledSpans` + - `binary_relations`: `BinaryRelation` annotations, converted from `SciDTBArgminDocument`'s `relations` + - labels: `support`, `attack`, `additional`, `detail`, `sequence` See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py) for the document type definitions. From f44870fbb339197a4390537423d76b32c3c07207 Mon Sep 17 00:00:00 2001 From: Arne Binder Date: Thu, 7 Nov 2024 14:07:52 +0100 Subject: [PATCH 41/42] improve usage example for sciarg --- dataset_builders/pie/sciarg/README.md | 30 ++++++++++++++++++++------- 1 file changed, 23 insertions(+), 7 deletions(-) diff --git a/dataset_builders/pie/sciarg/README.md b/dataset_builders/pie/sciarg/README.md index ee5bb318..d158efe2 100644 --- a/dataset_builders/pie/sciarg/README.md +++ b/dataset_builders/pie/sciarg/README.md @@ -42,17 +42,33 @@ See [PIE-Brat Data Schema](https://huggingface.co/datasets/pie/brat#data-schema) ### Usage ```python -from pie_datasets import load_dataset, builders +from pie_datasets import load_dataset +from pie_datasets.builders.brat import BratDocumentWithMergedSpans, BratDocument +from pytorch_ie.documents import TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions, TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions # load default version -datasets = load_dataset("pie/sciarg") -doc = datasets["train"][0] -assert isinstance(doc, builders.brat.BratDocumentWithMergedSpans) +dataset = load_dataset("pie/sciarg") +assert isinstance(dataset["train"][0], BratDocumentWithMergedSpans) + +# if required, normalize the document type (see section Document Converters below) +dataset_converted = dataset.to_document_type(TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions) +assert isinstance(dataset_converted["train"][0], TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions) + # load version with resolved parts_of_same relations -datasets = load_dataset("pie/sciarg", name='resolve_parts_of_same') -doc = datasets["train"][0] -assert isinstance(doc, builders.brat.BratDocument) +dataset = load_dataset("pie/sciarg", name='resolve_parts_of_same') +assert isinstance(dataset["train"][0], BratDocument) + +# if required, normalize the document type (see section Document Converters below) +dataset_converted = dataset.to_document_type(TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions) +assert isinstance(dataset_converted["train"][0], TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions) + +# get first relation in the first document +doc = dataset_converted["train"][0] +print(doc.binary_relations[0]) +# BinaryRelation(head=LabeledMultiSpan(slices=((15071, 15076),), label='data', score=1.0), tail=LabeledMultiSpan(slices=((14983, 15062),), label='background_claim', score=1.0), label='supports', score=1.0) +print(doc.binary_relations[0].resolve()) +# ('supports', (('data', ('[ 3 ]',)), ('background_claim', ('PSD and improved example-based schemes have been discussed in many publications',)))) ``` ### Document Converters From 189c1559c60bbe48effc00b1e1e7ce9a98805a62 Mon Sep 17 00:00:00 2001 From: Arne Binder Date: Thu, 7 Nov 2024 15:23:38 +0100 Subject: [PATCH 42/42] move usage example for sciarg --- dataset_builders/pie/sciarg/README.md | 63 +++++++++++++-------------- 1 file changed, 31 insertions(+), 32 deletions(-) diff --git a/dataset_builders/pie/sciarg/README.md b/dataset_builders/pie/sciarg/README.md index d158efe2..bfa5918f 100644 --- a/dataset_builders/pie/sciarg/README.md +++ b/dataset_builders/pie/sciarg/README.md @@ -4,6 +4,37 @@ This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for t Therefore, the `sciarg` dataset as described here follows the data structure from the [PIE brat dataset card](https://huggingface.co/datasets/pie/brat). +### Usage + +```python +from pie_datasets import load_dataset +from pie_datasets.builders.brat import BratDocumentWithMergedSpans, BratDocument +from pytorch_ie.documents import TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions, TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions + +# load default version +dataset = load_dataset("pie/sciarg") +assert isinstance(dataset["train"][0], BratDocumentWithMergedSpans) + +# if required, normalize the document type (see section Document Converters below) +dataset_converted = dataset.to_document_type(TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions) +assert isinstance(dataset_converted["train"][0], TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions) + +# load version with resolved parts_of_same relations +dataset = load_dataset("pie/sciarg", name='resolve_parts_of_same') +assert isinstance(dataset["train"][0], BratDocument) + +# if required, normalize the document type (see section Document Converters below) +dataset_converted = dataset.to_document_type(TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions) +assert isinstance(dataset_converted["train"][0], TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions) + +# get first relation in the first document +doc = dataset_converted["train"][0] +print(doc.binary_relations[0]) +# BinaryRelation(head=LabeledMultiSpan(slices=((15071, 15076),), label='data', score=1.0), tail=LabeledMultiSpan(slices=((14983, 15062),), label='background_claim', score=1.0), label='supports', score=1.0) +print(doc.binary_relations[0].resolve()) +# ('supports', (('data', ('[ 3 ]',)), ('background_claim', ('PSD and improved example-based schemes have been discussed in many publications',)))) +``` + ### Dataset Summary The SciArg dataset is an extension of the Dr. Inventor corpus (Fisas et al., [2015](https://aclanthology.org/W15-1605.pdf), [2016](https://aclanthology.org/L16-1492.pdf)) with an annotation layer containing @@ -39,38 +70,6 @@ are connected via the `parts_of_same` relations are converted to `LabeledMultiSp See [PIE-Brat Data Schema](https://huggingface.co/datasets/pie/brat#data-schema). -### Usage - -```python -from pie_datasets import load_dataset -from pie_datasets.builders.brat import BratDocumentWithMergedSpans, BratDocument -from pytorch_ie.documents import TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions, TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions - -# load default version -dataset = load_dataset("pie/sciarg") -assert isinstance(dataset["train"][0], BratDocumentWithMergedSpans) - -# if required, normalize the document type (see section Document Converters below) -dataset_converted = dataset.to_document_type(TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions) -assert isinstance(dataset_converted["train"][0], TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions) - - -# load version with resolved parts_of_same relations -dataset = load_dataset("pie/sciarg", name='resolve_parts_of_same') -assert isinstance(dataset["train"][0], BratDocument) - -# if required, normalize the document type (see section Document Converters below) -dataset_converted = dataset.to_document_type(TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions) -assert isinstance(dataset_converted["train"][0], TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions) - -# get first relation in the first document -doc = dataset_converted["train"][0] -print(doc.binary_relations[0]) -# BinaryRelation(head=LabeledMultiSpan(slices=((15071, 15076),), label='data', score=1.0), tail=LabeledMultiSpan(slices=((14983, 15062),), label='background_claim', score=1.0), label='supports', score=1.0) -print(doc.binary_relations[0].resolve()) -# ('supports', (('data', ('[ 3 ]',)), ('background_claim', ('PSD and improved example-based schemes have been discussed in many publications',)))) -``` - ### Document Converters The dataset provides document converters for the following target document types: