Datasets:

surrey-nlp
/

PLOD-unfiltered

@@ -1,39 +0,0 @@
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-*.arrow filter=lfs diff=lfs merge=lfs -text
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-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
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-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zstandard filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
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@@ -1,427 +0,0 @@
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PLOD-unfiltered.py DELETED Viewed

@@ -1,115 +0,0 @@
-import os
-import datasets
-from typing import List
-import json
-logger = datasets.logging.get_logger(__name__)
-_CITATION = """
-"""
-_DESCRIPTION = """
-This is the dataset repository for PLOD Dataset accepted to be published at LREC 2022.
-The dataset can help build sequence labelling models for the task Abbreviation Detection.
-"""
-class PLODunfilteredConfig(datasets.BuilderConfig):
-    """BuilderConfig for Conll2003"""
-    def __init__(self, **kwargs):
-        """BuilderConfig forConll2003.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(PLODunfilteredConfig, self).__init__(**kwargs)
-class PLODunfilteredConfig(datasets.GeneratorBasedBuilder):
-    """PLOD Unfiltered dataset."""
-    BUILDER_CONFIGS = [
-        PLODunfilteredConfig(name="PLODunfiltered", version=datasets.Version("0.0.2"), description="PLOD unfiltered 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-unfiltered/resolve/main/data/"
-    _URLS = {
-        "train": _URL + "PLOS-train70-unfiltered-pos_bio.json",
-        "dev": _URL + "PLOS-val15-unfiltered-pos_bio.json",
-        "test": _URL + "PLOS-test15-unfiltered-pos_bio.json"
-    }
-    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'],
-                }

data/PLOS-test15-unfiltered-pos_bio.json → PLODunfiltered/plod-unfiltered-test.parquet RENAMED Viewed

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+size 5118142

data/PLOS-train70-unfiltered-pos_bio.json → PLODunfiltered/plod-unfiltered-train.parquet RENAMED Viewed

@@ -1,3 +1,3 @@
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+size 23911685

data/PLOS-val15-unfiltered-pos_bio.json → PLODunfiltered/plod-unfiltered-validation.parquet RENAMED Viewed

@@ -1,3 +1,3 @@
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-oid sha256:b76bc7fc64fc70f8cf11c37dc44c3fdf98afd2415b20e2a1024aa9b045aa4604
-size 18374815

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+oid sha256:9a304abdf894a2722c3aff855ce3e7218681150cf01faac3bb482e9ec3abc079
+size 5144784

README.md DELETED Viewed

@@ -1,173 +0,0 @@
----
-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-an-abbreviation-detection-dataset-for
-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-unfiltered
-## 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-an-abbreviation
-- **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.

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