fixed typos

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.
 
 <details>
 <summary>Command</summary>
@@ -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.
 
 <details>
 <summary>Command</summary>
@@ -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.
 
 <details>
 <summary>Command</summary>

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.
 
 <details>
 <summary>Command</summary>
@@ -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.
 
 <details>
 <summary>Command</summary>
@@ -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.
 
 <details>
 <summary>Command</summary>

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.
 
 <details>
 <summary>Command</summary>
@@ -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.
 
 <details>
 <summary>Command</summary>
@@ -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.
 
 <details>
 <summary>Command</summary>

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.
 
 <details>
 <summary>Command</summary>
@@ -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.
 
 <details>
 <summary>Command</summary>
@@ -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.
 
 <details>
 <summary>Command</summary>

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.
 
 <details>
 <summary>Command</summary>
@@ -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.
 
 <details>
 <summary>Command</summary>
@@ -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.
 
 <details>
 <summary>Command</summary>

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.
 
 <details>
 <summary>Command</summary>
@@ -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.
 
 <details>
 <summary>Command</summary>
@@ -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.
 
 <details>
 <summary>Command</summary>