add reader for BTC

broad_twitter_corpus.py

@@ -0,0 +1,165 @@
+# coding=utf-8
+# Copyright 2020 HuggingFace Datasets Authors.
+#
+# 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
+"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""
+
+import os
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@inproceedings{derczynski2016broad,
+  title={Broad twitter corpus: A diverse named entity recognition resource},
+  author={Derczynski, Leon and Bontcheva, Kalina and Roberts, Ian},
+  booktitle={Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
+  pages={1169--1179},
+  year={2016}
+}
+"""
+
+_DESCRIPTION = """\
+This is the Broad Twitter corpus, a dataset of tweets collected over stratified times, places and social uses. 
+The goal is to represent a broad range of activities, giving a dataset more representative of the language used 
+in this hardest of social media formats to process. Further, the BTC is annotated for named entities.
+
+For more details see [https://aclanthology.org/C16-1111/](https://aclanthology.org/C16-1111/)
+"""
+
+_URL = "https://github.com/GateNLP/broad_twitter_corpus/archive/refs/heads/master.zip"
+_subpath = "broad_twitter_corpus-master/"
+_A_FILE = _subpath + "a.conll"
+_B_FILE = _subpath + "b.conll"
+_E_FILE = _subpath + "e.conll"
+_F_FILE = _subpath + "f.conll"
+_G_FILE = _subpath + "g.conll"
+_H_FILE = _subpath + "h.conll"
+
+# _TRAINING_FILE = "train.txt"
+_DEV_FILE = _H_FILE
+_TEST_FILE = _F_FILE
+
+
+class BroadTwitterCorpusConfig(datasets.BuilderConfig):
+    """BuilderConfig for BroadTwitterCorpus"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for BroadTwitterCorpus.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(BroadTwitterCorpusConfig, self).__init__(**kwargs)
+
+
+class BroadTwitterCorpus(datasets.GeneratorBasedBuilder):
+    """BroadTwitterCorpus dataset."""
+
+    BUILDER_CONFIGS = [
+        BroadTwitterCorpusConfig(name="broad-twitter-corpus", version=datasets.Version("1.0.0"), description="Broad Twitter Corpus"),
+    ]
+
+    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-PER",
+                                "I-PER",
+                                "B-ORG",
+                                "I-ORG",
+                                "B-LOC",
+                                "I-LOC",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://aclanthology.org/C16-1111/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        downloaded_file = dl_manager.download_and_extract(_URL)
+
+        data_files = {
+            "a": os.path.join(downloaded_file, _A_FILE),
+            "b": os.path.join(downloaded_file, _B_FILE),
+            "e": os.path.join(downloaded_file, _E_FILE),
+            "f": os.path.join(downloaded_file, _F_FILE),
+            "g": os.path.join(downloaded_file, _G_FILE),
+            "h": os.path.join(downloaded_file, _H_FILE),
+            "dev": os.path.join(downloaded_file, _DEV_FILE),
+            "test": os.path.join(downloaded_file, _TEST_FILE),
+        }
+
+        """
+        btc_section_a = datasets.SplitGenerator(name="BTC_A", gen_kwargs={"filepath": data_files["a"]})
+        btc_section_b = datasets.SplitGenerator(name="BTC_B", gen_kwargs={"filepath": data_files["b"]})
+        btc_section_e = datasets.SplitGenerator(name="BTC_E", gen_kwargs={"filepath": data_files["e"]})
+        btc_section_f = datasets.SplitGenerator(name="BTC_F", gen_kwargs={"filepath": data_files["f"]})
+        btc_section_g = datasets.SplitGenerator(name="BTC_G", gen_kwargs={"filepath": data_files["g"]})
+        btc_section_h = datasets.SplitGenerator(name="BTC_H", gen_kwargs={"filepath": data_files["h"]})
+        """
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN,
+                gen_kwargs={"filepaths": [data_files['a'], data_files['b'], data_files['e'], data_files['g']]}
+                ),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": [data_files["dev"]]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepaths": [data_files["test"]]}),
+        ]
+
+    def _generate_examples(self, filepaths):
+        guid = 0
+        for filepath in filepaths:
+            with open(filepath, encoding="utf-8") as f:
+                logger.info("⏳ Generating examples from = %s", filepath)
+                tokens = []
+                ner_tags = []
+                for line in f:
+                    if line.startswith("-DOCSTART-") or line.strip() == "" or line == "\n":
+                        if tokens:
+                            yield guid, {
+                                "id": str(guid),
+                                "tokens": tokens,
+                                "ner_tags": ner_tags,
+                            }
+                            guid += 1
+                            tokens = []
+                            ner_tags = []
+                    else:
+                        # btc entries are tab separated
+                        fields = line.split("\t")
+                        tokens.append(fields[0])
+                        ner_tags.append(fields[1].rstrip())
+                # last example
+                yield guid, {
+                    "id": str(guid),
+                    "tokens": tokens,
+                    "ner_tags": ner_tags,
+                }
+                guid += 1 # for when files roll over

dataset_infos.json

@@ -0,0 +1 @@
+{"broad-twitter-corpus": {"description": "This is the Broad Twitter corpus, a dataset of tweets collected over stratified times, places and social uses. \nThe goal is to represent a broad range of activities, giving a dataset more representative of the language used \nin this hardest of social media formats to process. Further, the BTC is annotated for named entities.\n\nFor more details see [https://aclanthology.org/C16-1111/](https://aclanthology.org/C16-1111/)\n", "citation": "@inproceedings{derczynski2016broad,\n  title={Broad twitter corpus: A diverse named entity recognition resource},\n  author={Derczynski, Leon and Bontcheva, Kalina and Roberts, Ian},\n  booktitle={Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},\n  pages={1169--1179},\n  year={2016}\n}\n", "homepage": "https://aclanthology.org/C16-1111/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 7, "names": ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"], "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "broad_twitter_corpus", "config_name": "broad-twitter-corpus", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1522066, "num_examples": 5342, "dataset_name": "broad_twitter_corpus"}, "validation": {"name": "validation", "num_bytes": 514159, "num_examples": 2002, "dataset_name": "broad_twitter_corpus"}, "test": {"name": "test", "num_bytes": 621542, "num_examples": 2002, "dataset_name": "broad_twitter_corpus"}}, "download_checksums": {"https://github.com/GateNLP/broad_twitter_corpus/archive/refs/heads/master.zip": {"num_bytes": 39344594, "checksum": "4e1d1a7d0d9e7563f5df2adf078c25a7305ab6a5e74eadae123881e4d175a12f"}}, "download_size": 39344594, "post_processing_size": null, "dataset_size": 2657767, "size_in_bytes": 42002361}}