| # PIE Dataset Card for "argmicro" | |
| 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(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=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()) | |
| # ('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 | |
| The dataset contains two `BuilderConfig`'s: | |
| - `de`: with the original texts collection in German | |
| - `en`: with the English-translated texts | |
| ## Data Schema | |
| The document type for this dataset is `ArgMicroDocument` which defines the following data fields: | |
| - `text` (str) | |
| - `id` (str, optional) | |
| - `topic_id` (str, optional) | |
| - `metadata` (dictionary, optional) | |
| and the following annotation layers: | |
| - `stance` (annotation type: `Label`) | |
| - description: A document may contain one of these `stance` labels: `pro`, `con`, `unclear`, or no label when it is undefined (see [here](https://huggingface.co/datasets/DFKI-SLT/argmicro/blob/main/argmicro.py#L35) for reference). | |
| - `edus` (annotation type: `Span`, target: `text`) | |
| - `adus` (annotation type: `LabeledAnnotationCollection`, target: `edus`) | |
| - description: each element of `adus` may consist of several entries from `edus`, so we require `LabeledAnnotationCollection` as annotation type. This is originally indicated by `seg` edges in the data. | |
| - `LabeledAnnotationCollection` has the following fields: | |
| - `annotations` (annotation type: `Span`, target: `text`) | |
| - `label` (str, optional), values: `opp`, `pro` (see [here](https://huggingface.co/datasets/DFKI-SLT/argmicro/blob/main/argmicro.py#L36)) | |
| - `relations` (annotation type: `MultiRelation`, target: `adus`) | |
| - description: Undercut (`und`) relations originally target other relations (i.e. edges), but we let them target the `head` of the targeted relation instead. The original state can be deterministically reconstructed by taking the label into account. Furthermore, the head of additional source (`add`) relations are integrated into the head of the target relation (note that this propagates along `und` relations). We model this with `MultiRelation`s whose `head` and `tail` are of type `LabeledAnnotationCollection`. | |
| - `MultiRelation` has the following fields: | |
| - `head` (tuple, annotation type: `LabeledAnnotationCollection`, target: `adus`) | |
| - `tail` (tuple, annotation type: `LabeledAnnotationCollection`, target: `adus`) | |
| - `label` (str, optional), values: `sup`, `exa`, `reb`, `und` (see [here](https://huggingface.co/datasets/DFKI-SLT/argmicro/blob/main/argmicro.py#L37) for reference, but note that helper relations `seg` and `add` are not there anymore, see above). | |
| See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/annotations.py) for the annotation type definitions. | |
| ## Document Converters | |
| The dataset provides document converters for the following target document types: | |
| - `pytorch_ie.documents.TextDocumentWithLabeledSpansAndBinaryRelations` | |
| - `LabeledSpans`, converted from `ArgMicroDocument`'s `adus` | |
| - labels: `opp`, `pro` | |
| - if an ADU contains multiple spans (i.e. EDUs), we take the start of the first EDU and the end of the last EDU as the boundaries of the new `LabeledSpan`. We also raise exceptions if any newly created `LabeledSpan`s overlap. | |
| - `BinraryRelations`, converted from `ArgMicroDocument`'s `relations` | |
| - labels: `sup`, `reb`, `und`, `joint`, `exa` | |
| - if the `head` or `tail` consists of multiple `adus`, then we build `BinaryRelation`s with all `head`-`tail` combinations and take the label from the original relation. Then, we build `BinaryRelations`' with label `joint` between each component that previously belongs to the same `head` or `tail`, respectively. | |
| - `metadata`, we keep the `ArgMicroDocument`'s `metadata`, but `stance` and `topic_id`. | |
| 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 | |
| ``` | |
| 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=argmicro_base metric=relation_argument_token_distances | |
| ``` | |
| </details> | |
| | | 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 | | |
| <details> | |
| <summary>Histogram (split: train, 112 documents)</summary> | |
|  | |
| </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=argmicro_base metric=span_lengths_tokens | |
| ``` | |
| </details> | |
| | statistics | train | | |
| | :--------- | -----: | | |
| | no. doc | 112 | | |
| | len | 576 | | |
| | mean | 16.365 | | |
| | std | 6.545 | | |
| | min | 4 | | |
| | max | 41 | | |
| <details> | |
| <summary>Histogram (split: train, 112 documents)</summary> | |
|  | |
| </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=argmicro_base metric=count_text_tokens | |
| ``` | |
| </details> | |
| | statistics | train | | |
| | :--------- | -----: | | |
| | no. doc | 112 | | |
| | mean | 84.161 | | |
| | std | 22.596 | | |
| | min | 36 | | |
| | max | 153 | | |
| <details> | |
| <summary>Histogram (split: train, 112 documents)</summary> | |
|  | |
| </details> | |