From e68f60d08189d9ba3e91a03b71e3cd20d24c869f Mon Sep 17 00:00:00 2001 From: Rin Ldallit <88072261+idalr@users.noreply.github.com> Date: Thu, 7 Nov 2024 15:37:04 +0100 Subject: [PATCH] Collecting statistics from AM dataset (#100) * added statistics: Relation_argument_outer_token_distance.md * minor fix * micro fix * micro fix * micro fix * added commands and collapsed all histograms * minor edits * added text_length_tokens.md and minor edit * micro fix * add span_lengths_tokens.md * edited texts in markdowns * micro fix * added relation_argument_outer_token_distance_per_label.md * micro fix * deleted ~~relation_argument_outer_token_distance.md~~ * moved collected statistics content to pie/abstrct/readme.md * minor fix * edited text in abstrct/readme * minor edit abstrct/readme * removed abstrct histograms from statistics folder * moved content and files to aae2.readme, and removed from statistics folder * minor fix * minor edit * moved content and files to argmicro/readme.md * moved content and files to cdcp/readme.md * minor fix * moved content and files to scidtb_argmin/readme.md * minor fix * moved content and files to sciarg/readme.md * deleted AM_statistics folder * minor edit * fixed typos * minor adjustments * minor adjustments * improve usage examples * add usage example to argmicro * fix argmicro usage example * add cdcp usage example * add usage example to scidtb_argmin minor and fix * add details to document converter * improve usage example for sciarg * move usage example for sciarg --------- Co-authored-by: Arne Binder --- dataset_builders/pie/aae2/README.md | 184 +++++++++++- .../pie/aae2/img/rtd-label_aae2_test.png | Bin 0 -> 15394 bytes .../pie/aae2/img/rtd-label_aae2_train.png | Bin 0 -> 15308 bytes .../pie/aae2/img/slt_aae2_test.png | Bin 0 -> 12394 bytes .../pie/aae2/img/slt_aae2_train.png | Bin 0 -> 12774 bytes .../pie/aae2/img/tl_aae2_test.png | Bin 0 -> 11504 bytes .../pie/aae2/img/tl_aae2_train.png | Bin 0 -> 12595 bytes dataset_builders/pie/abstrct/README.md | 279 ++++++++++++++++-- .../abstrct/img/rtd-label_abs-glu_test.png | Bin 0 -> 17093 bytes .../abstrct/img/rtd-label_abs-mix_test.png | Bin 0 -> 15611 bytes .../pie/abstrct/img/rtd-label_abs-neo_dev.png | Bin 0 -> 14896 bytes .../abstrct/img/rtd-label_abs-neo_test.png | Bin 0 -> 17181 bytes .../abstrct/img/rtd-label_abs-neo_train.png | Bin 0 -> 17315 bytes .../pie/abstrct/img/slt_abs-glu_test.png | Bin 0 -> 11444 bytes .../pie/abstrct/img/slt_abs-mix_test.png | Bin 0 -> 12819 bytes .../pie/abstrct/img/slt_abs-neo_dev.png | Bin 0 -> 12192 bytes .../pie/abstrct/img/slt_abs-neo_test.png | Bin 0 -> 11118 bytes .../pie/abstrct/img/slt_abs-neo_train.png | Bin 0 -> 13025 bytes .../pie/abstrct/img/tl_abs-glu_test.png | Bin 0 -> 11715 bytes .../pie/abstrct/img/tl_abs-mix_test.png | Bin 0 -> 12049 bytes .../pie/abstrct/img/tl_abs-neo_dev.png | Bin 0 -> 11669 bytes .../pie/abstrct/img/tl_abs-neo_test.png | Bin 0 -> 12109 bytes .../pie/abstrct/img/tl_abs-neo_train.png | Bin 0 -> 12017 bytes dataset_builders/pie/argmicro/README.md | 140 +++++++++ .../pie/argmicro/img/rtd-label_argmicro.png | Bin 0 -> 16120 bytes .../pie/argmicro/img/slt_argmicro.png | Bin 0 -> 12058 bytes .../pie/argmicro/img/tl_argmicro.png | Bin 0 -> 10254 bytes dataset_builders/pie/cdcp/README.md | 165 +++++++++++ .../pie/cdcp/img/rtd-label_cdcp_test.png | Bin 0 -> 15391 bytes .../pie/cdcp/img/rtd-label_cdcp_train.png | Bin 0 -> 16507 bytes .../pie/cdcp/img/slt_cdcp_test.png | Bin 0 -> 12298 bytes .../pie/cdcp/img/slt_cdcp_train.png | Bin 0 -> 13167 bytes .../pie/cdcp/img/tl_cdcp_test.png | Bin 0 -> 12278 bytes .../pie/cdcp/img/tl_cdcp_train.png | Bin 0 -> 13037 bytes dataset_builders/pie/sciarg/README.md | 182 +++++++++++- .../pie/sciarg/img/rtd-label_sciarg.png | Bin 0 -> 17272 bytes .../pie/sciarg/img/slt_sciarg.png | Bin 0 -> 13302 bytes dataset_builders/pie/sciarg/img/tl_sciarg.png | Bin 0 -> 11574 bytes dataset_builders/pie/scidtb_argmin/README.md | 138 +++++++++ .../img/rtd-label_scitdb-argmin.png | Bin 0 -> 16871 bytes .../scidtb_argmin/img/slt_scitdb-argmin.png | Bin 0 -> 12514 bytes .../scidtb_argmin/img/tl_scidtb-argmin.png | Bin 0 -> 12076 bytes 42 files changed, 1041 insertions(+), 47 deletions(-) create mode 100644 dataset_builders/pie/aae2/img/rtd-label_aae2_test.png create mode 100644 dataset_builders/pie/aae2/img/rtd-label_aae2_train.png create mode 100644 dataset_builders/pie/aae2/img/slt_aae2_test.png create mode 100644 dataset_builders/pie/aae2/img/slt_aae2_train.png create mode 100644 dataset_builders/pie/aae2/img/tl_aae2_test.png create mode 100644 dataset_builders/pie/aae2/img/tl_aae2_train.png create mode 100644 dataset_builders/pie/abstrct/img/rtd-label_abs-glu_test.png create mode 100644 dataset_builders/pie/abstrct/img/rtd-label_abs-mix_test.png create mode 100644 dataset_builders/pie/abstrct/img/rtd-label_abs-neo_dev.png create mode 100644 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dataset_builders/pie/argmicro/img/slt_argmicro.png create mode 100644 dataset_builders/pie/argmicro/img/tl_argmicro.png create mode 100644 dataset_builders/pie/cdcp/img/rtd-label_cdcp_test.png create mode 100644 dataset_builders/pie/cdcp/img/rtd-label_cdcp_train.png create mode 100644 dataset_builders/pie/cdcp/img/slt_cdcp_test.png create mode 100644 dataset_builders/pie/cdcp/img/slt_cdcp_train.png create mode 100644 dataset_builders/pie/cdcp/img/tl_cdcp_test.png create mode 100644 dataset_builders/pie/cdcp/img/tl_cdcp_train.png create mode 100644 dataset_builders/pie/sciarg/img/rtd-label_sciarg.png create mode 100644 dataset_builders/pie/sciarg/img/slt_sciarg.png create mode 100644 dataset_builders/pie/sciarg/img/tl_sciarg.png create mode 100644 dataset_builders/pie/scidtb_argmin/img/rtd-label_scitdb-argmin.png create mode 100644 dataset_builders/pie/scidtb_argmin/img/slt_scitdb-argmin.png create mode 100644 dataset_builders/pie/scidtb_argmin/img/tl_scidtb-argmin.png diff --git a/dataset_builders/pie/aae2/README.md b/dataset_builders/pie/aae2/README.md index f47a7f9f..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 | @@ -109,7 +121,7 @@ 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 +141,154 @@ 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/aae2_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 collapsible, 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 collapsible, 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 collapsible, 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/dataset_builders/pie/aae2/img/rtd-label_aae2_test.png b/dataset_builders/pie/aae2/img/rtd-label_aae2_test.png new file mode 100644 index 0000000000000000000000000000000000000000..d62218f8793710f656b64215f22afcabbf58999b GIT binary patch literal 15394 zcmeHucT`j9`fk*5P#EE;AR~3uLl;nK0s<-m(rf4>Dgx4_cVa_S1f=&~69Pyllz=dZ zv`~{s2}ODWp%Yrd-Eqb_Gw00QbM9H|-amfpvX*Xl%=hlEKKpr}=jFAYj@l_k4n`0N zbV~j19Rm>PXdno5Wb(xKz$Z#s?>B(U5f1~k+n^E**CKH9o!u?%TOd$bB-6qD^3ynrA_a9ZEsW4S7e#H)Pb(NFFrGOcnkz;&KCs60FqXl0&d>R zGV+2zPkQusfRCIaS{Oi}dmgNcz)i}=m-$t*&hESTva_}e#s&!q6ceG)^G89T$(D!Q zhI>(3b{n1g*`8*u=g>xN?cXhq+uJUjgJ&9ydVYS3iBDt8dJ=6B>+;d=k^u12%PA|n zo0p0=8eR&Qd-xH5%4(L{A~W+wS90;GIr%*4vzx5&@uJ&`0`bMxYMzyqO$QsYNAKzY zKQ8>3+d4YIC+E*IYy}vfv?6!gDGckyjP7NwhQER2&3qP=06P7ok4L<o>?knKEYZ>AUn3A8WVU8bCw`f46jMeOEa@XHr|P1}alyioQ$!9KL-}h2R7$}O z6XGTV8}8wY=#5Wr8oGyxR^u1cie#!q;%FQgNvczrv8fdqas+X=?1HICYE z(zZGFwWH&8h^3QoE6@2NN3WK~Hw9y#4sKb$bQ-dr<}o1{*p^&3qc)x?sn?e&+0p45 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zFs!fVgBVRoXu@vmNAZJu(=m=X%co&W-4NBGJ3Dy{-xT-a_%bbj{Zd$SOi`3&(4b+n zTMa&K(cAwlwT(v+5-tE;GLAyyg~Su-Ij$%%^)GhZ5E&{xlxxK39^xSsla-a9vsIAJ z@-P-W_=1W>sS(SU44WNcD~dv}+#w~Orn^o%jK(QAD!r=g4zYKgP)KULHc+S^nloL# zXCKyPjJYtLkhK0Y&RJx(|rS`vR}NYYa=;EORUGge1X= zq-!h)%7M{i8xA2nP~6p4)vCoM*FexR(R11u`_H?hUr#KjrO{Q*e)DlteF>Y1|o*$2{Lz|u|chtFBSyEV0C zTGd+hVr12!q3VFdAnlC=+dX+p@SlnB? zU?s0}g!-+G!uzCK%^qWIuQ-=>afVK-i>aZkK9_=-nb~)Cp6qhFAIPf!;qM$-C~5Hv zyk{>=$UJPXHTNri1H$My)+O9r=`o6j#e8(S&*1FG-(xEFg!8OMcurrREm)&6cH+_R z+8RP-)Yl4;VXzDKJ$==xS5P1@Vr(@9?^@HXP^J0d(3DCWwg0(JZu*M2j1bcumdZbq zqT&=p2|u5@hJ2Tqo~_eE9STB?QZU%hhQooi_EF1~0E3WaY-xW9s|mQ$8}(J<083K35w?rPBg(u!$4bSO;G z{wfq$_q#7Vv1EodC2iCE8qsn_M`hl4OiF(;f%-&2br_OORb9m2t+R?}jw<&7eD79} n9kYNN_)yCCU1Hqxf}wg}d$nJ +Command + +``` +python src/evaluate_documents.py dataset=abstrct_base metric=relation_argument_token_distances +``` + + + +##### 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 collapsible, 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%2Fslt_abs-neo_train.png) + +
+
+ Histogram (split: neoplasm_dev, 50 documents) + +![slt_abs-neo_dev.png](img%2Fslt_abs-neo_dev.png) + +
+
+ Histogram (split: neoplasm_test, 100 documents) + +![slt_abs-neo_test.png](img%2Fslt_abs-neo_test.png) + +
+
+ Histogram (split: glucoma_test, 100 documents) + +![slt_abs-glu_test.png](img%2Fslt_abs-glu_test.png) + +
+
+ Histogram (split: mixed_test, 100 documents) + +![slt_abs-mix_test.png](img%2Fslt_abs-mix_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 collapsible, showing the distribution of these token 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%2Ftl_abs-neo_train.png) + +
+
+ Histogram (split: neoplasm_dev, 50 documents) + +![tl_abs-neo_dev.png](img%2Ftl_abs-neo_dev.png) + +
+
+ Histogram (split: neoplasm_test, 100 documents) + +![tl_abs-neo_test.png](img%2Ftl_abs-neo_test.png) + +
+
+ Histogram (split: glucoma_test, 100 documents) + +![tl_abs-glu_test.png](img%2Ftl_abs-glu_test.png) + +
+
+ Histogram (split: mixed_test, 100 documents) + +![tl_abs-mix_test.png](img%2Ftl_abs-mix_test.png) -See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py) for the document type -definitions. +
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z#D=cSgXuS-6Rxl6LJ`##ca&7fbK$B3{{GHGeXlN`x5zSQDC;X&$L2@b6=(%_n^)o4 zzkC<7X8d&t1;sd_87#V;Uh#R(nLx46aC?M36;4RYdJXeD&=+=zC=&k8hKVOdlje?= z)1G|l&cO(HvyoRXy4C4LBy=LQmeX|LBg{aZl0d|NQmhMZ{kfzt=6sO9e6retIvdmE z^`g|S;Jm)}3@16ja&;tlYljPQum?{N%cC%^2NYE1c_dyfuu~SztF?j9Uscpxo##=W zn9~RtSM)F`Dy*5QO`|CqTy*kZWwU~gq`}JT5Wim@JUWx#hb+;#tNgOdq4gzvjYX9q zx0y(hY@^=&{mNmW_t*(>X|qz~8M#f*)L(9YOXO+=liHqppvy{7)Ev@kVv64MV(b^o zxsnqn5-#Io+lXQg2uG>1PMmct zoMlT}58;h?MU}+u$CWr6yOn2XG_ID9<-YirN*T$RQkpFu8~eqJZ;>C*Dmw;Cgv>8^ ziP^5*Gt5cW*!9tBED+@FnWF>Cc~f4BL&g~*#kdsNxf^iIL$XkqWcwP6u^ zl9iG2)c_yw5(^Vss>9IBz%>?i#%;@x-Oi-2>5=Shbo(ZBK!8gvlQBpDr za-xQB-q6#a(6ZF3pvZs1w?R)qC0`9qd@{Eb+8o7@)emQ$#J=1bpL8f~_$)q-AFm99 zKYc-&O*cMW5V*sQwr7Jd!>uO~`)bchin=T=Nr@J^%%w7sx-AWL z&(+JgzfuMEe0C%$w74mK$XdQ%>DbsFF~aTrEj(CwCuOp}Qa~=Bk}6Ahat*)IUX~u+ z-1UO*1pS4Q?6iqJwQp8B^h^M5P1bj$7{$sG7%KCT+FSZI8Q$4z$e7kK>_OzJRAb@9 zHgedKwi#jZW@xNcZgSsXH}i$b{@Ac{JI-5fU&{v z9ravEnFC7cwd9xemG7S3%9#$ep+Z|Yblh8x +Command + +``` +python src/evaluate_documents.py dataset=argmicro_base metric=relation_argument_token_distances +``` + + + +| | 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 collapsible, 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 collapsible, 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) + +
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ztDv6mRejoqm3KmZBF=sc-&TyXC&MbwT#~@-8~}G9KUn=i3#WZ5ouWzcQl&2*I9}gz U +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 collapsible, 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 collapsible, 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) + +
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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 @@ -39,21 +70,25 @@ 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 +### Document Converters -```python -from pie_datasets import load_dataset, builders +The dataset provides document converters for the following target document types: -# load default version -datasets = load_dataset("pie/sciarg") -doc = datasets["train"][0] -assert isinstance(doc, builders.brat.BratDocumentWithMergedSpans) +- `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` -# 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) -``` +See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py) for the document type +definitions. ### Data Splits @@ -133,6 +168,13 @@ 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 +185,14 @@ 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 +225,111 @@ 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 collapsible, 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 collapsible, 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 collapsible, 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) + +
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z4xCuSDCo08QqnC(A|wNN4Bo@RQPR%l=)nQZ!Un))?jJvVvu)meeq~6pB&d-kmnb6>WN=v3)a^JEwwj; zPcze%ZPwCSfRSBP{og%^K+@y-SOiQAW%*XP`_4v=w@UO_wcOwshw`F@DIbK?__Pv{ zT8nCzEDAvxqxA+30)hJtY0m=!C){jCubh9uvVEK99(05|f$nO!y^+M8hhX3;d&lDT z`IYTN^==|RNF5Bs0YvWOsB>afRyJC_nWzy)(kgd@q{8`raharwo0Ht_`+=?UWIwo7 z01wywqZy*^++L+w&QEOa)t<8 literal 0 HcmV?d00001 diff --git a/dataset_builders/pie/scidtb_argmin/README.md b/dataset_builders/pie/scidtb_argmin/README.md index e5235c75..cb577a6b 100644 --- a/dataset_builders/pie/scidtb_argmin/README.md +++ b/dataset_builders/pie/scidtb_argmin/README.md @@ -3,6 +3,27 @@ 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: @@ -31,3 +52,120 @@ 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=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/scidtb_argmin + revision: 335a8e6168919d7f204c6920eceb96745dbd161b +``` + +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 collapsible, 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 collapsible, 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 collapsible, 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) + +
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