use pie-modules instead of pytorch-ie (#5)

- from https://github.com/ArneBinder/pie-datasets/pull/204 (3f57c3ca11fb82fcb04489d17ee4c169a52795d7)

README.md

@@ -9,7 +9,7 @@ Therefore, the `aae2` dataset as described here follows the data structure from
 ```python
 from pie_datasets import load_dataset
 from pie_datasets.builders.brat import BratDocumentWithMergedSpans
-from pytorch_ie.documents import TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions
+from pie_modules.documents import TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions
 
 # load default version
 dataset = load_dataset("pie/aae2")
@@ -53,11 +53,11 @@ See [PIE-Brat Data Schema](https://huggingface.co/datasets/pie/brat#data-schema)
 
 ### Data Splits
 
-| Statistics                                                       |                      Train |                     Test |
-| ---------------------------------------------------------------- | -------------------------: | -----------------------: |
-| No. of document                                                  |                        322 |                       80 |
-| Components     <br/>- `MajorClaim`<br/>- `Claim`<br/>- `Premise` | <br/>598<br/>1202<br/>3023 | <br/>153<br/>304<br/>809 |
-| Relations\*<br/>- `supports`<br/>- `attacks`                     |          <br/>3820<br/>405 |         <br/>1021<br/>92 |
+| Statistics                                                   |                      Train |                     Test |
+| ------------------------------------------------------------ | -------------------------: | -----------------------: |
+| No. of document                                              |                        322 |                       80 |
+| Components <br/>- `MajorClaim`<br/>- `Claim`<br/>- `Premise` | <br/>598<br/>1202<br/>3023 | <br/>153<br/>304<br/>809 |
+| Relations\*<br/>- `supports`<br/>- `attacks`                 |          <br/>3820<br/>405 |         <br/>1021<br/>92 |
 
 \* included all relations between claims and premises and all claim attributions.
 
@@ -90,17 +90,19 @@ See further statistics in Stab & Gurevych (2017), p. 650, Table A.1.
 
 See further description in Stab & Gurevych 2017, p.627 and the [annotation guideline](https://github.com/ArneBinder/pie-datasets/blob/db94035602610cefca2b1678aa2fe4455c96155d/data/datasets/ArgumentAnnotatedEssays-2.0/guideline.pdf).
 
-**Note that** relations between `MajorClaim` and  `Claim` were not annotated; however, each claim is annotated with an `Attribute` annotation with value `for` or `against` - which indicates the relation between itself and `MajorClaim`. In addition, when two non-related `Claim` 's appear in one paragraph, there is also no relations to one another. An example of a document is shown here below.
+**Note that** relations between `MajorClaim` and `Claim` were not annotated; however, each claim is annotated with an `Attribute` annotation with value `for` or `against` - which indicates the relation between itself and `MajorClaim`. In addition, when two non-related `Claim` 's appear in one paragraph, there is also no relations to one another. An example of a document is shown here below.
 
 #### Example
 
-![Example](img/sg17f2.png)
+![Structure](img/sg17f2.png)
+
+![Example](img/aae2_train_47.png)
 
 ### Document Converters
 
 The dataset provides document converters for the following target document types:
 
-- `pytorch_ie.documents.TextDocumentWithLabeledSpansAndBinaryRelations` with layers:
+- `pie_modules.documents.TextDocumentWithLabeledSpansAndBinaryRelations` with layers:
   - `labeled_spans`: `LabeledSpan` annotations, converted from `BratDocumentWithMergedSpans`'s `spans`
     - labels: `MajorClaim`, `Claim`, `Premise`
   - `binary_relations`: `BinaryRelation` annotations, converted from `BratDocumentWithMergedSpans`'s `relations`
@@ -112,13 +114,13 @@ The dataset provides document converters for the following target document types
         - build a `supports` or `attacks` relation from each `Claim` to every `MajorClaim`
         - no relations between each `MajorClaim`
     - labels: `supports`, `attacks`, and `semantically_same` if `connect_first`
-- `pytorch_ie.documents.TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions` with layers:
+- `pie_modules.documents.TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions` with layers:
   - `labeled_spans`, as above
   - `binary_relations`, as above
   - `labeled_partitions`, `LabeledSpan` annotations, created from splitting `BratDocumentWithMergedSpans`'s `text` at new lines (`\n`).
     - every partition is labeled as `paragraph`
 
-See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py) for the document type
+See [here](https://github.com/ArneBinder/pie-modules/blob/main/src/pie_modules/documents.py) for the document type
 definitions.
 
 #### Relation Label Statistics after Document Conversion
@@ -162,7 +164,7 @@ input:
   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 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)).
 
 For relation-label statistics, we collect those from the default relation conversion method, i.e., `connect_first`, resulting in three distinct relation labels.
 
@@ -351,7 +353,7 @@ Three non-native speakers; one of the three being an expert annotator.
 
 ### Social Impact of Dataset
 
-"\[Computational Argumentation\] have
+"[Computational Argumentation] have
 broad application potential in various areas such as legal decision support (Mochales-Palau and Moens 2009), information retrieval (Carstens and Toni 2015), policy making (Sardianos et al. 2015), and debating technologies (Levy et al. 2014; Rinott et al.
 2015)." (p. 619)
 
@@ -366,7 +368,7 @@ The relations between claims and major claims are not explicitly annotated.
 "The proportion of non-argumentative text amounts to 47,474 tokens (32.2%) and
 1,631 sentences (22.9%). The number of sentences with several argument components
 is 583, of which 302 include several components with different types (e.g., a claim followed by premise)...
-\[T\]he identification of argument components requires the
+[T]he identification of argument components requires the
 separation of argumentative from non-argumentative text units and the recognition of
 component boundaries at the token level...The proportion of paragraphs with unlinked
 argument components (e.g., unsupported claims without incoming relations) is 421

aae2.py

@@ -2,9 +2,9 @@ import os
 from typing import Dict
 
 import pandas as pd
+from pie_modules.annotations import BinaryRelation
 from pie_modules.document.processing import RegexPartitioner
-from pytorch_ie.annotations import BinaryRelation
-from pytorch_ie.documents import (
+from pie_modules.documents import (
     TextDocumentWithLabeledSpansAndBinaryRelations,
     TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions,
 )

requirements.txt

@@ -1,2 +1,2 @@
-pie-datasets>=0.8.0,<0.11.0
-pie-modules>=0.8.3,<0.12.0
+pie-datasets>=0.10.11,<0.11.0
+pie-modules>=0.15.9,<0.16.0