Add loading script and README file

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 [Dataset Name]
+
+## 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

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+# 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://raw.githubusercontent.com/haotianteng/nacl22/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,
+                }