adding reader py, data desc and readme

LICENSE

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PLOD-filtered.py

@@ -0,0 +1,115 @@
+import os
+
+import datasets
+from typing import List
+import json
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """
+"""
+
+_DESCRIPTION = """
+This is the dataset repository for a subset of the PLOD Dataset published at LREC 2022 (from cleaned portion accepted at LREC COLING 2024).
+The dataset can help build sequence labelling models for the task Abbreviation and Long form Detection.
+"""
+
+class PLODfilteredConfig(datasets.BuilderConfig):
+    """BuilderConfig for Conll2003"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig forConll2003.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(PLODfilteredConfig, self).__init__(**kwargs)
+
+
+class PLODfilteredConfig(datasets.GeneratorBasedBuilder):
+    """PLOD Filtered dataset."""
+
+    BUILDER_CONFIGS = [
+        PLODfilteredConfig(name="PLODfiltered", version=datasets.Version("0.0.2"), description="PLOD filtered dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "pos_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "ADJ",
+                                "ADP",
+                                "ADV",
+                                "AUX",
+                                "CONJ",
+                                "CCONJ",
+                                "DET",
+                                "INTJ",
+                                "NOUN",
+                                "NUM",
+                                "PART",
+                                "PRON",
+                                "PROPN",
+                                "PUNCT",
+                                "SCONJ",
+                                "SYM",
+                                "VERB",
+                                "X",
+                                "SPACE"
+                            ]
+                        )
+                    ),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "B-O",
+                                "B-AC",
+                                "I-AC",
+                                "B-LF",
+                                "I-LF"
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/surrey-nlp/PLOD-AbbreviationDetection",
+            citation=_CITATION,
+        )
+
+    _URL = "https://huggingface.co/datasets/surrey-nlp/PLOD-CW/resolve/main/data/"
+    _URLS = {
+        "train": _URL + "data/train.conll",
+        "dev": _URL + "data/train.conll",
+        "test": _URL + "data/train.conll"
+    }
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        urls_to_download = self._URLS
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]})
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        with open(filepath) as f:
+            plod = json.load(f)
+            for object in plod:
+                id_ = int(object['id'])
+                yield id_, {
+                    "id": str(id_),
+                    "tokens": object['tokens'],
+                    "pos_tags": object['pos_tags'],
+                    "ner_tags": object['ner_tags'],
+                }

README.md

@@ -1,3 +1,171 @@
 ---
-license: cc-by-sa-3.0
+annotations_creators:
+- Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan 
+language_creators:
+- found
+language:
+- en
+license: "cc-by-sa-4.0"
+multilinguality:
+- monolingual
+paperswithcode_id: plod-filtered
+pretty_name: 'PLOD: An Abbreviation Detection Dataset'
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- token-classification
+task_ids:
+- abbreviation-detection
 ---
+
+# PLOD: An Abbreviation Detection Dataset  
+
+This is the repository for PLOD Dataset published at LREC 2022. The dataset can help build sequence labelling models for the task Abbreviation Detection.
+
+### Dataset
+
+We provide two variants of our dataset - Filtered and Unfiltered. They are described in our paper here.
+
+1. The Filtered version can be accessed via [Huggingface Datasets here](https://huggingface.co/datasets/surrey-nlp/PLOD-filtered) and a [CONLL format is present here](https://github.com/surrey-nlp/PLOD-AbbreviationDetection).<br/>
+
+2. The Unfiltered version can be accessed via [Huggingface Datasets here](https://huggingface.co/datasets/surrey-nlp/PLOD-unfiltered) and a [CONLL format is present here](https://github.com/surrey-nlp/PLOD-AbbreviationDetection).<br/>
+
+3. The [SDU Shared Task](https://sites.google.com/view/sdu-aaai22/home) data we use for zero-shot testing is [available here](https://huggingface.co/datasets/surrey-nlp/SDU-test).
+
+# Dataset Card for PLOD-filtered
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [Needs More Information]
+- **Repository:** https://github.com/surrey-nlp/PLOD-AbbreviationDetection
+- **Paper:** https://arxiv.org/abs/2204.12061
+- **Leaderboard:** https://paperswithcode.com/sota/abbreviationdetection-on-plod-filtered
+- **Point of Contact:** [Diptesh Kanojia](mailto:d.kanojia@surrey.ac.uk)
+
+### Dataset Summary
+
+This PLOD Dataset is an English-language dataset of abbreviations and their long-forms tagged in text. The dataset has been collected for research from the PLOS journals indexing of abbreviations and long-forms in the text. This dataset was created to support the Natural Language Processing task of abbreviation detection and covers the scientific domain. 
+
+### Supported Tasks and Leaderboards
+
+This dataset primarily supports the Abbreviation Detection Task. It has also been tested on a train+dev split provided by the Acronym Detection Shared Task organized as a part of the Scientific Document Understanding (SDU) workshop at AAAI 2022.
+
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+A typical data point comprises an ID, a set of `tokens` present in the text, a set of `pos_tags` for the corresponding tokens obtained via Spacy NER, and a set of `ner_tags` which are limited to `AC` for `Acronym` and `LF` for `long-forms`.
+
+An example from the dataset:
+{'id': '1',
+ 'tokens': ['Study', '-', 'specific', 'risk', 'ratios', '(', 'RRs', ')', 'and', 'mean', 'BW', 'differences', 'were', 'calculated', 'using', 'linear', 'and', 'log', '-', 'binomial', 'regression', 'models', 'controlling', 'for', 'confounding', 'using', 'inverse', 'probability', 'of', 'treatment', 'weights', '(', 'IPTW', ')', 'truncated', 'at', 'the', '1st', 'and', '99th', 'percentiles', '.'],
+ 'pos_tags': [8, 13, 0, 8, 8, 13, 12, 13, 5, 0, 12, 8, 3, 16, 16, 0, 5, 0, 13, 0, 8, 8, 16, 1, 8, 16, 0, 8, 1, 8, 8, 13, 12, 13, 16, 1, 6, 0, 5, 0, 8, 13],
+ 'ner_tags': [0, 0, 0, 3, 4, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+}
+
+### Data Fields
+
+- id: the row identifier for the dataset point.
+- tokens: The tokens contained in the text.
+- pos_tags: the Part-of-Speech tags obtained for the corresponding token above from Spacy NER.
+- ner_tags: The tags for abbreviations and long-forms.
+
+
+### Data Splits
+
+|            | Train   | Valid | Test |
+| -----      | ------ | -----  | ---- |
+| Filtered   | 112652 |  24140 | 24140|
+| Unfiltered | 113860 |  24399 | 24399|
+
+
+## Dataset Creation
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Extracting the data from PLOS Journals online and then tokenization, normalization.
+
+#### Who are the source language producers?
+
+PLOS Journal
+
+## Additional Information
+
+### Dataset Curators
+
+The dataset was initially created by Leonardo Zilio, Hadeel Saadany, Prashant Sharma,
+Diptesh Kanojia, Constantin Orasan.
+
+### Licensing Information
+
+CC-BY-SA 4.0
+
+### Citation Information
+
+[Needs More Information]
+
+### Installation
+
+We use the custom NER pipeline in the [spaCy transformers](https://spacy.io/universe/project/spacy-transformers) library to train our models. This library supports training via any pre-trained language models available at the :rocket: [HuggingFace repository](https://huggingface.co/).<br/>
+Please see the instructions at these websites to setup your own custom training with our dataset to reproduce the experiments using Spacy.
+
+OR<br/>
+
+However, you can also reproduce the experiments via the Python notebook we [provide here](https://github.com/surrey-nlp/PLOD-AbbreviationDetection/blob/main/nbs/fine_tuning_abbr_det.ipynb) which uses HuggingFace Trainer class to perform the same experiments. The exact hyperparameters can be obtained from the models readme cards linked below. Before starting, please perform the following steps:
+
+```bash
+git clone https://github.com/surrey-nlp/PLOD-AbbreviationDetection
+cd PLOD-AbbreviationDetection
+pip install -r requirements.txt
+```
+
+Now, you can use the notebook to reproduce the experiments.
+
+### Model(s)
+
+
+Our best performing models are hosted on the HuggingFace models repository
+
+| Models | [`PLOD - Unfiltered`](https://huggingface.co/datasets/surrey-nlp/PLOD-unfiltered) | [`PLOD - Filtered`](https://huggingface.co/datasets/surrey-nlp/PLOD-filtered) | Description |
+| --- | :---: | :---: | --- |
+| [RoBERTa<sub>large</sub>](https://huggingface.co/roberta-large) | [RoBERTa<sub>large</sub>-finetuned-abbr](https://huggingface.co/surrey-nlp/roberta-large-finetuned-abbr) | -soon- | Fine-tuning on the RoBERTa<sub>large</sub> language model |
+| [RoBERTa<sub>base</sub>](https://huggingface.co/roberta-base) | -soon- | [RoBERTa<sub>base</sub>-finetuned-abbr](https://huggingface.co/surrey-nlp/roberta-large-finetuned-abbr) | Fine-tuning on the RoBERTa<sub>base</sub> language model |
+| [AlBERT<sub>large-v2</sub>](https://huggingface.co/albert-large-v2) | [AlBERT<sub>large-v2</sub>-finetuned-abbDet](https://huggingface.co/surrey-nlp/albert-large-v2-finetuned-abbDet) | -soon- | Fine-tuning on the AlBERT<sub>large-v2</sub> language model |
+
+On the link provided above, the model(s) can be used with the help of the Inference API via the web-browser itself. We have placed some examples with the API for testing.<br/>
+
+### Usage
+
+You can use the HuggingFace Model link above to find the instructions for using this model in Python locally using the notebook provided in the Git repo.