Update fdner.py

fdner.py

@@ -1,153 +1,153 @@
-# 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{tjong-kim-sang-de-meulder-2003-introduction,
-    title = "Introduction to the Fault_Detection_Ner Task: Language-Independent Named Entity Recognition",
-    author = "Tian Jie",
-    year = "2022"
-}
-"""
-
-_DESCRIPTION = """\
-用于故障诊断领域相关知识的命名实体识别语料
-"""
-
-_URL = "https://cdn-lfs.huggingface.co/datasets/leonadase/fdner/89a87eacfebc06862ac4b5a356c35430dfdf8ef2f0f2e0d9ff5e02ce6c117474"
-_TRAINING_FILE = "train.txt"
-_DEV_FILE = "valid.txt"
-_TEST_FILE = "test.txt"
-
-
-class fdnerConfig(datasets.BuilderConfig):
-    """BuilderConfig for fdNer"""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for fdNer.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        logger.info("Generating examples from 1")
-        super(fdnerConfig, self).__init__(**kwargs)
-
-
-class fdner(datasets.GeneratorBasedBuilder):
-    """fdNer dataset."""
-
-    BUILDER_CONFIGS = [
-        fdnerConfig(name="fdner", version=datasets.Version("1.0.0"), description="fdner dataset"),
-    ]
-
-    def _info(self):
-        logger.info("Generating examples from 1")
-        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-EN",
-                                "I-EN",
-                                "B-STRUC",
-                                "I-STRUC",
-                                "B-CHA",
-                                "I-CHA",
-                                "B-KIND",
-                                "I-KIND",
-                                "B-ADV",
-                                "I-ADV",
-                                "B-DISA",
-                                "I-DISA",
-                                "B-METH",
-                                "I-METH",
-                                "B-NUM",
-                                "I-NUM",
-                                "B-PRO",
-                                "I-PRO",
-                                "B-THE",
-                                "I-THE",
-                                "B-DEF",
-                                "I-DEF",
-                                "B-FUC",
-                                "I-FUC",
-                            ]
-                        )
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            # homepage="https://www.aclweb.org/anthology/W03-0419/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        logger.info("Generating examples from 2")    
-        """Returns SplitGenerators."""
-        downloaded_file = dl_manager.download_and_extract(_URL)
-        data_files = {
-            "train": os.path.join(downloaded_file, _TRAINING_FILE),
-            "dev": os.path.join(downloaded_file, _DEV_FILE),
-            "test": os.path.join(downloaded_file, _TEST_FILE),
-        }
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
-        ]
-    def _generate_examples(self, filepath):
-        logger.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
-            yield guid, {
-                "id": str(guid),
-                "tokens": tokens,
-                "ner_tags": ner_tags,
-            }
+# 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{tjong-kim-sang-de-meulder-2003-introduction,
+    title = "Introduction to the Fault_Detection_Ner Task: Language-Independent Named Entity Recognition",
+    author = "Tian Jie",
+    year = "2022"
+}
+"""
+
+_DESCRIPTION = """\
+用于故障诊断领域相关知识的命名实体识别语料
+"""
+
+_URL = "https://huggingface.co/datasets/leonadase/fdner/resolve/main/fdner.zip"
+_TRAINING_FILE = "train.txt"
+_DEV_FILE = "valid.txt"
+_TEST_FILE = "test.txt"
+
+
+class fdnerConfig(datasets.BuilderConfig):
+    """BuilderConfig for fdNer"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for fdNer.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        logger.info("Generating examples from 1")
+        super(fdnerConfig, self).__init__(**kwargs)
+
+
+class fdner(datasets.GeneratorBasedBuilder):
+    """fdNer dataset."""
+
+    BUILDER_CONFIGS = [
+        fdnerConfig(name="fdner", version=datasets.Version("1.0.0"), description="fdner dataset"),
+    ]
+
+    def _info(self):
+        logger.info("Generating examples from 1")
+        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-EN",
+                                "I-EN",
+                                "B-STRUC",
+                                "I-STRUC",
+                                "B-CHA",
+                                "I-CHA",
+                                "B-KIND",
+                                "I-KIND",
+                                "B-ADV",
+                                "I-ADV",
+                                "B-DISA",
+                                "I-DISA",
+                                "B-METH",
+                                "I-METH",
+                                "B-NUM",
+                                "I-NUM",
+                                "B-PRO",
+                                "I-PRO",
+                                "B-THE",
+                                "I-THE",
+                                "B-DEF",
+                                "I-DEF",
+                                "B-FUC",
+                                "I-FUC",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            # homepage="https://www.aclweb.org/anthology/W03-0419/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        logger.info("Generating examples from 2")    
+        """Returns SplitGenerators."""
+        downloaded_file = dl_manager.download_and_extract(_URL)
+        data_files = {
+            "train": os.path.join(downloaded_file, _TRAINING_FILE),
+            "dev": os.path.join(downloaded_file, _DEV_FILE),
+            "test": os.path.join(downloaded_file, _TEST_FILE),
+        }
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
+        ]
+    def _generate_examples(self, filepath):
+        logger.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
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "ner_tags": ner_tags,
+            }