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ubb-endava-conll-assistant-ner-only-misc-v2.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
+
+
+import os
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = ""
+
+_DESCRIPTION = ""
+
+#_URL = "."
+_TRAINING_FILE = "train.txt"
+_DEV_FILE = "validation.txt"
+_TEST_FILE = "test.txt"
+
+
+class UBBDemoConfig(datasets.BuilderConfig):
+    """BuilderConfig for UBBDemo"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for UBBDemo.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(UBBDemoConfig, self).__init__(**kwargs)
+
+
+class UBBDemo(datasets.GeneratorBasedBuilder):
+    """UBBDemo dataset."""
+
+    BUILDER_CONFIGS = [
+        UBBDemoConfig(name="UBBDemo", version=datasets.Version("1.0.0"), description="UBBDemo 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-PER",
+                                "I-PER",
+                                "B-ORG",
+                                "I-ORG",
+                                "B-LOC",
+                                "I-LOC",
+                                "B-MISC",
+                                "I-MISC",
+                       
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        
+        path = "./"
+        data_files = {
+            "train": os.path.join(path, _TRAINING_FILE),
+            "validation": os.path.join(path, _DEV_FILE),
+            "test": os.path.join(path, _TEST_FILE),
+        }
+
+        downloaded_file = dl_manager.download_and_extract(data_files)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file ["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_file ["validation"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_file ["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        print("I am here" + 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:
+                    # UBBDemo tokens are space separated
+                    splits = line.split(" ")
+                    tokens.append(splits[0])
+                    ner_tags.append(splits[3].rstrip())
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "ner_tags": ner_tags,
+            }