Create nlp-model-tune.py

nlp-model-tune.py

@@ -0,0 +1,170 @@
+# 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
+import datasets
+
+
+_DESCRIPTION = ""
+_HOMEPAGE_URL = ""
+_CITATION = None
+_TRAIN_URL = "https://huggingface.co/datasets/ayuhamaro/nlp-model-tune/blob/main/train"
+
+
+class WeiboNERCorpus(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    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-CARDINAL",
+                                "B-DATE",
+                                "B-EVENT",
+                                "B-FAC",
+                                "B-GPE",
+                                "B-LANGUAGE",
+                                "B-LAW",
+                                "B-LOC",
+                                "B-MONEY",
+                                "B-NORP",
+                                "B-ORDINAL",
+                                "B-ORG",
+                                "B-PERCENT",
+                                "B-PERSON",
+                                "B-PRODUCT",
+                                "B-QUANTITY",
+                                "B-TIME",
+                                "B-WORK_OF_ART",
+                                "I-CARDINAL",
+                                "I-DATE",
+                                "I-EVENT",
+                                "I-FAC",
+                                "I-GPE",
+                                "I-LANGUAGE",
+                                "I-LAW",
+                                "I-LOC",
+                                "I-MONEY",
+                                "I-NORP",
+                                "I-ORDINAL",
+                                "I-ORG",
+                                "I-PERCENT",
+                                "I-PERSON",
+                                "I-PRODUCT",
+                                "I-QUANTITY",
+                                "I-TIME",
+                                "I-WORK_OF_ART",
+                                "E-CARDINAL",
+                                "E-DATE",
+                                "E-EVENT",
+                                "E-FAC",
+                                "E-GPE",
+                                "E-LANGUAGE",
+                                "E-LAW",
+                                "E-LOC",
+                                "E-MONEY",
+                                "E-NORP",
+                                "E-ORDINAL",
+                                "E-ORG",
+                                "E-PERCENT",
+                                "E-PERSON",
+                                "E-PRODUCT",
+                                "E-QUANTITY",
+                                "E-TIME",
+                                "E-WORK_OF_ART",
+                                "S-CARDINAL",
+                                "S-DATE",
+                                "S-EVENT",
+                                "S-FAC",
+                                "S-GPE",
+                                "S-LANGUAGE",
+                                "S-LAW",
+                                "S-LOC",
+                                "S-MONEY",
+                                "S-NORP",
+                                "S-ORDINAL",
+                                "S-ORG",
+                                "S-PERCENT",
+                                "S-PERSON",
+                                "S-PRODUCT",
+                                "S-QUANTITY",
+                                "S-TIME",
+                                "S-WORK_OF_ART"
+                            ]
+                        )
+                    ),
+                },
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        train_path = dl_manager.download_and_extract(_TRAIN_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_path": train_path},
+            )
+        ]
+
+    def _generate_examples(self, data_path):
+        sentence_counter = 0
+        with open(data_path, encoding="utf-8") as f:
+            current_words = []
+            current_labels = []
+            for row in f:
+                row = row.rstrip()
+                row_split = row.split("\t")
+                if len(row_split) == 2:
+                    token, label = row_split
+                    current_words.append(token)
+                    current_labels.append(label)
+                else:
+                    if not current_words:
+                        continue
+                    assert len(current_words) == len(current_labels), "word len doesnt match label length"
+                    sentence = (
+                        sentence_counter,
+                        {
+                            "id": str(sentence_counter),
+                            "tokens": current_words,
+                            "ner_tags": current_labels,
+                        },
+                    )
+                    sentence_counter += 1
+                    current_words = []
+                    current_labels = []
+                    yield sentence
+
+            # if something remains:
+            if current_words:
+                sentence = (
+                    sentence_counter,
+                    {
+                        "id": str(sentence_counter),
+                        "tokens": current_words,
+                        "ner_tags": current_labels,
+                    },
+                )
+                yield sentence