From f41a72071c6e813a3fad815e600ed69370051de8 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] 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 6647e829..a91c843c 100644 --- a/dataset_builders/pie/sciarg/README.md +++ b/dataset_builders/pie/sciarg/README.md @@ -156,7 +156,7 @@ 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 @@ -187,7 +187,7 @@ 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 @@ -219,7 +219,7 @@ 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.
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