https://github.com/ArneBinder/pie-datasets/pull/100

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
 
@@ -65,7 +76,7 @@ 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.
 
@@ -105,6 +116,240 @@ Morio et al. ([2022](https://aclanthology.org/2022.tacl-1.37.pdf); p. 642, Table
 
 (Mayer et al. 2020, p.2110)
 
+#### Example
+
+![abstr-sam.png](img%2Fabstr-sam.png)
+
+### 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=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
+input:
+  _target_: pie_datasets.DatasetDict.load_dataset
+  path: pie/abstrct
+  revision: 277dc703fd78614635e86fe57c636b54931538b2
+```
+
+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.
+
+<details>
+<summary>Command</summary>
+
+```
+python src/evaluate_documents.py dataset=abstrct_base metric=relation_argument_token_distances
+```
+
+</details>
+
+##### 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 |
+
+<details>
+  <summary>Histogram (split: neoplasm_train, 350 documents)</summary>
+
+![img_2.png](img/rtd-label_abs-neo_train.png)
+
+</details>
+
+##### 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 |
+
+<details>
+  <summary>Histogram (split: neoplasm_dev, 50 documents)</summary>
+
+![img_3.png](img/rtd-label_abs-neo_dev.png)
+
+</details>
+
+##### 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 |
+
+<details>
+  <summary>Histogram (split: neoplasm_test, 100 documents)</summary>
+
+![img_4.png](img/rtd-label_abs-neo_test.png)
+
+</details>
+
+##### 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 |
+
+<details>
+  <summary>Histogram (split: glaucoma_test, 100 documents)</summary>
+
+![img_5.png](img/rtd-label_abs-glu_test.png)
+
+</details>
+
+##### 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 |
+
+<details>
+  <summary>Histogram (split: mixed_test, 100 documents)</summary>
+
+![img_6.png](img/rtd-label_abs-mix_test.png)
+
+</details>
+
+#### 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.
+
+<details>
+<summary>Command</summary>
+
+```
+python src/evaluate_documents.py dataset=abstrct_base metric=span_lengths_tokens
+```
+
+</details>
+
+| 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 |
+
+<details>
+  <summary>Histogram (split: neoplasm_train, 350 documents)</summary>
+
+![slt_abs-neo_train.png](img%2Fslt_abs-neo_train.png)
+
+</details>
+  <details>
+  <summary>Histogram (split: neoplasm_dev, 50 documents)</summary>
+
+![slt_abs-neo_dev.png](img%2Fslt_abs-neo_dev.png)
+
+</details>
+  <details>
+  <summary>Histogram (split: neoplasm_test, 100 documents)</summary>
+
+![slt_abs-neo_test.png](img%2Fslt_abs-neo_test.png)
+
+</details>
+  <details>
+  <summary>Histogram (split: glucoma_test, 100 documents)</summary>
+
+![slt_abs-glu_test.png](img%2Fslt_abs-glu_test.png)
+
+</details>
+  <details>
+  <summary>Histogram (split: mixed_test, 100 documents)</summary>
+
+![slt_abs-mix_test.png](img%2Fslt_abs-mix_test.png)
+
+</details>
+
+#### 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.
+
+<details>
+<summary>Command</summary>
+
+```
+python src/evaluate_documents.py dataset=abstrct_base metric=count_text_tokens
+```
+
+</details>
+
+| 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 |
+
+<details>
+  <summary>Histogram (split: neoplasm_train, 350 documents)</summary>
+
+![tl_abs-neo_train.png](img%2Ftl_abs-neo_train.png)
+
+</details>
+  <details>
+  <summary>Histogram (split: neoplasm_dev, 50 documents)</summary>
+
+![tl_abs-neo_dev.png](img%2Ftl_abs-neo_dev.png)
+
+</details>
+  <details>
+  <summary>Histogram (split: neoplasm_test, 100 documents)</summary>
+
+![tl_abs-neo_test.png](img%2Ftl_abs-neo_test.png)
+
+</details>
+  <details>
+  <summary>Histogram (split: glucoma_test, 100 documents)</summary>
+
+![tl_abs-glu_test.png](img%2Ftl_abs-glu_test.png)
+
+</details>
+  <details>
+  <summary>Histogram (split: mixed_test, 100 documents)</summary>
+
+![tl_abs-mix_test.png](img%2Ftl_abs-mix_test.png)
+
+</details>
+
 ## Dataset Creation
 
 ### Curation Rationale

requirements.txt

@@ -1 +1 @@
-pie-datasets>=0.4.0,<0.9.0
+pie-datasets>=0.4.0,<0.11.0