Update files from the datasets library (from 1.0.2)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.2

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.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
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

conll2000.py

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+# 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-2000 Shared Task: Chunking"""
+
+import logging
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{tksbuchholz2000conll,
+   author     = "Tjong Kim Sang, Erik F. and Sabine Buchholz",
+   title      = "Introduction to the CoNLL-2000 Shared Task: Chunking",
+   editor     = "Claire Cardie and Walter Daelemans and Claire
+                 Nedellec and Tjong Kim Sang, Erik",
+   booktitle  = "Proceedings of CoNLL-2000 and LLL-2000",
+   publisher  = "Lisbon, Portugal",
+   pages      = "127--132",
+   year       = "2000"
+}
+"""
+
+_DESCRIPTION = """\
+ Text chunking consists of dividing a text in syntactically correlated parts of words. For example, the sentence
+ He reckons the current account deficit will narrow to only # 1.8 billion in September . can be divided as follows:
+[NP He ] [VP reckons ] [NP the current account deficit ] [VP will narrow ] [PP to ] [NP only # 1.8 billion ]
+[PP in ] [NP September ] .
+
+Text chunking is an intermediate step towards full parsing. It was the shared task for CoNLL-2000. Training and test
+data for this task is available. This data consists of the same partitions of the Wall Street Journal corpus (WSJ)
+as the widely used data for noun phrase chunking: sections 15-18 as training data (211727 tokens) and section 20 as
+test data (47377 tokens). The annotation of the data has been derived from the WSJ corpus by a program written by
+Sabine Buchholz from Tilburg University, The Netherlands.
+"""
+
+_URL = "https://github.com/teropa/nlp/raw/master/resources/corpora/conll2000/"
+_TRAINING_FILE = "train.txt"
+_TEST_FILE = "test.txt"
+
+
+class Conll2000Config(datasets.BuilderConfig):
+    """BuilderConfig for Conll2000"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig forConll2000.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(Conll2000Config, self).__init__(**kwargs)
+
+
+class Conll2000(datasets.GeneratorBasedBuilder):
+    """Conll2000 dataset."""
+
+    BUILDER_CONFIGS = [
+        Conll2000Config(name="conll2000", version=datasets.Version("1.0.0"), description="Conll2000 dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "words": datasets.Sequence(datasets.Value("string")),
+                    "pos": datasets.Sequence(datasets.Value("string")),
+                    "chunk": datasets.Sequence(datasets.Value("string")),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://www.clips.uantwerpen.be/conll2000/chunking/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_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.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
+            words = []
+            pos = []
+            chunk = []
+            for line in f:
+                if line == "" or line == "\n":
+                    if words:
+                        yield guid, {"id": str(guid), "words": words, "pos": pos, "chunk": chunk}
+                        guid += 1
+                        words = []
+                        pos = []
+                        chunk = []
+                else:
+                    # conll2000 tokens are space separated
+                    splits = line.split(" ")
+                    words.append(splits[0])
+                    pos.append(splits[1])
+                    chunk.append(splits[2].rstrip())
+            # last example
+            yield guid, {"id": str(guid), "words": words, "pos": pos, "chunk": chunk}

dataset_infos.json

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+{"conll2000": {"description": " Text chunking consists of dividing a text in syntactically correlated parts of words. For example, the sentence\n He reckons the current account deficit will narrow to only # 1.8 billion in September . can be divided as follows:\n[NP He ] [VP reckons ] [NP the current account deficit ] [VP will narrow ] [PP to ] [NP only # 1.8 billion ]\n[PP in ] [NP September ] .\n\nText chunking is an intermediate step towards full parsing. It was the shared task for CoNLL-2000. Training and test\ndata for this task is available. This data consists of the same partitions of the Wall Street Journal corpus (WSJ)\nas the widely used data for noun phrase chunking: sections 15-18 as training data (211727 tokens) and section 20 as\ntest data (47377 tokens). The annotation of the data has been derived from the WSJ corpus by a program written by\nSabine Buchholz from Tilburg University, The Netherlands.\n", "citation": "@inproceedings{tksbuchholz2000conll,\n   author     = \"Tjong Kim Sang, Erik F. and Sabine Buchholz\",\n   title      = \"Introduction to the CoNLL-2000 Shared Task: Chunking\",\n   editor     = \"Claire Cardie and Walter Daelemans and Claire\n                 Nedellec and Tjong Kim Sang, Erik\",\n   booktitle  = \"Proceedings of CoNLL-2000 and LLL-2000\",\n   publisher  = \"Lisbon, Portugal\",\n   pages      = \"127--132\",\n   year       = \"2000\"\n}\n", "homepage": "https://www.clips.uantwerpen.be/conll2000/chunking/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "words": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "pos": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "chunk": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "conll2000", "config_name": "conll2000", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4916429, "num_examples": 8937, "dataset_name": "conll2000"}, "test": {"name": "test", "num_bytes": 1102955, "num_examples": 2013, "dataset_name": "conll2000"}}, "download_checksums": {"https://github.com/teropa/nlp/raw/master/resources/corpora/conll2000/train.txt": {"num_bytes": 2842164, "checksum": "82033cd7a72b209923a98007793e8f9de3abc1c8b79d646c50648eb949b87cea"}, "https://github.com/teropa/nlp/raw/master/resources/corpora/conll2000/test.txt": {"num_bytes": 639396, "checksum": "73b7b1e565fa75a1e22fe52ecdf41b6624d6f59dacb591d44252bf4d692b1628"}}, "download_size": 3481560, "post_processing_size": null, "dataset_size": 6019384, "size_in_bytes": 9500944}}

dummy/conll2000/1.0.0/dummy_data.zip

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+size 994