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README.md

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----
-annotations_creators:
-- expert-generated
-language:
-- en
-language_creators:
-- crowdsourced
-license:
-- afl-3.0
-multilinguality:
-- monolingual
-pretty_name: sci_NER_naacl
-size_categories:
-- 1K<n<10K
-source_datasets:
-- original
-tags:
-- acl
-- sciBERT
-- sci
-- acl
-- '11711'
-task_categories:
-- token-classification
-task_ids:
-- named-entity-recognition
----
-
-# Dataset Card for [naacl2022]
-
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [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)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:**
-- **Repository:**
-- **Paper:**
-- **Leaderboard:**
-- **Point of Contact:**
-
-### Dataset Summary
-
-This is a named entity recognition dataset annotated for the science entity recognition task, a [project](https://github.com/neubig/nlp-from-scratch-assignment-2022) from the CMU 11-711 course.
-
-### Supported Tasks and Leaderboards
-
-NER task.
-
-### Languages
-
-English
-
-## Dataset Structure
-
-### Data Instances
-
-A sample of the dataset
-{'id': '0',
-'tokens': ['We', 'sample', '50', 'negative', 'cases', 'from', 'T5LARGE', '+', 'GenMC', 'for', 'each', 'dataset'],
-'ner_tags':['O', 'O', 'O', 'O', 'O', 'O', 'B-MethodName', 'O', 'B-MethodName', 'O', 'O', 'O']}
-
-### Data Fields
-
-id,tokens,ner_tags
-
-- `id`: a `string` feature give the sample index.
-- `tokens`: a `list` of `string` features give the sequence.
-- `ner_tags`: a `list` of classification labels for each token in the sentence, with possible values including 
-`O` (0), `B-MethodName` (1), `I-MethodName` (2), `B-HyperparameterName` (3),`I-HyperparameterName` (4),`B-HyperparameterValue` (5),`I-HyperparameterValue` (6),`B-MetricName` (7),`I-MetricName` (8),`B-MetricValue` (9),`I-MetricValue` (10),`B-TaskName` (11),`I-TaskName` (12),`B-DatasetName` (13),`I-DatasetName` (14).
-
-
-### Data Splits
-
-Data split into
-train.txt
-dev.txt
-test.txt
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed]
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
-
-[More Information Needed]
-
-### Annotations
-
-#### Annotation process
-
-The data is annotated by using labelstudio, the papers are collected from TACL and ACL 2022 conferences.
-
-#### Who are the annotators?
-
-Xiaoyue Cui and Haotian Teng annotated the datasets.
-
-### Personal and Sensitive Information
-
-[More Information Needed]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
-
-[More Information Needed]
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed]
-
-### Licensing Information
-
-[More Information Needed]
-
-### Citation Information
-
-[More Information Needed]
-
-### Contributions
-
-Thanks to [@xcui297](https://github.com/xcui297); [@haotianteng](https://github.com/haotianteng) for adding this dataset.

naacl2022.py

@@ -1,136 +0,0 @@
-# coding=utf-8
-# Copyright 2022 Haotian Teng
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Lint as: python3
-"""CrossWeigh: Training Named Entity Tagger from Imperfect Annotations"""
-
-import logging
-
-import datasets
-
-
-_CITATION = """\
-"""
-
-_DESCRIPTION = """\
-NACL22 is a dataset labelled for Science Entity Recognition task, which is a subtask of NER task. 
-The text is from 2022 conference papers collected from ACL anthology. 
-The dataset is collected by Haotian Teng and Xiaoyue Cui. 
-Annotation standard can be found here https://github.com/neubig/nlp-from-scratch-assignment-2022/blob/main/annotation_standard.md
-"""
-
-_URL = "https://github.com/haotianteng/nacl22/raw/master/"
-_TRAINING_FILE = "train.text"
-_DEV_FILE = "dev.text"
-_TEST_FILE = "test.text"#Test dataset need to be added.
-
-
-class nacl22Config(datasets.BuilderConfig):
-    """BuilderConfig for NACL2022"""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for NACL2022.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(nacl22Config, self).__init__(**kwargs)
-
-
-class nacl22(datasets.GeneratorBasedBuilder):
-    """NACL2022 dataset."""
-
-    BUILDER_CONFIGS = [
-        nacl22Config(name="nacl22", version=datasets.Version("1.0.0"), description="nacl22 dataset"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "tokens": datasets.Sequence(datasets.Value("string")),
-                    "ner_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "O",
-                                "B-MethodName",
-                                "I-MethodName",
-                                "B-HyperparameterName",
-                                "I-HyperparameterName",
-                                "B-HyperparameterValue",
-                                "I-HyperparameterValue",
-                                "B-MetricName",
-                                "I-MetricName",
-                                "B-MetricValue",
-                                "I-MetricValue",
-                                "B-TaskName",
-                                "I-TaskName",
-                                "B-DatasetName",
-                                "I-DatasetName",
-                            ]
-                        )
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://github.com/neubig/nlp-from-scratch-assignment-2022",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        urls_to_download = {
-            "train": f"{_URL}{_TRAINING_FILE}",
-            "dev": f"{_URL}{_DEV_FILE}",
-            "test": f"{_URL}{_TEST_FILE}",
-        }
-        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):
-        logging.info("⏳ Generating examples from = %s", filepath)
-        with open(filepath, encoding="utf-8") as f:
-            guid = 0
-            tokens = []
-            ner_tags = []
-            for line in f:
-                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
-                    if tokens:
-                        yield guid, {
-                            "id": str(guid),
-                            "tokens": tokens,
-                            "ner_tags": ner_tags,
-                        }
-                        guid += 1
-                        tokens = []
-                        ner_tags = []
-                else:
-                    # conll2003 tokens are space separated
-                    splits = line.split(" ")
-                    tokens.append(splits[0])
-                    ner_tags.append(splits[-1].rstrip())
-            # last example
-            if tokens:
-                yield guid, {
-                    "id": str(guid),
-                    "tokens": tokens,
-                    "ner_tags": ner_tags,
-                }

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