Upload Biobert_json.py

Biobert_json.py

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+# coding=utf-8
+#
+# 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 Biobert NER Shared Task:  Named Entity Recognition"""
+import datasets
+import json
+
+
+logger = datasets.logging.get_logger(__name__)
+
+_DESCRIPTION = """\
+Este es un dataset biomédico Biobert para el español con 29 etiquetas"""
+
+_URL="data56/"
+_TRAINING_FILE = "train.json"
+_DEV_FILE = "valid.json"
+_TEST_FILE = "test.json"
+
+
+class Biobert_json_Config(datasets.BuilderConfig):
+    
+
+    def __init__(self, **kwargs):
+        super(Biobert_json_Config, self).__init__(**kwargs)
+
+
+class Conll2003(datasets.GeneratorBasedBuilder):
+    """Conll2003 dataset."""
+
+    BUILDER_CONFIGS = [
+        Biobert_json_Config(name="Biobert_json", version=datasets.Version("1.0.0"), description="Biobert_json dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                   # "id": datasets.Value("string"),
+                    "sentencia": datasets.Sequence(datasets.Value("string")),
+                    "tag": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "B_CANCER_CONCEPT",
+                                "B_CHEMOTHERAPY",
+                                "B_DATE",
+                                "B_DRUG",
+                                "B_FAMILY",
+                                "B_FREQ",
+                                "B_IMPLICIT_DATE",
+                                "B_INTERVAL",
+                                "B_METRIC",
+                                "B_OCURRENCE_EVENT",
+                                "B_QUANTITY",
+                                "B_RADIOTHERAPY",
+                                "B_SMOKER_STATUS",
+                                "B_STAGE", 
+                                "B_SURGERY",
+                                "B_TNM",
+                                "I_CANCER_CONCEPT",
+                                "I_DATE",
+                                "I_DRUG",
+                                "I_FAMILY",
+                                "I_FREQ",
+                                "I_IMPLICIT_DATE",
+                                "I_INTERVAL",
+                                "I_METRIC",
+                                "I_OCURRENCE_EVENT",
+                                "I_SMOKER_STATUS",
+                                "I_STAGE", 
+                                "I_SURGERY",
+                                "I_TNM",
+                                "O", 
+                                
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_FILE}",
+            "val": f"{_URL}{_DEV_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.VALIDATION, gen_kwargs={"filepath": downloaded_files["val"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            for line in f:  
+                record = json.loads(line)   
+                yield guid, record
+                guid += 1
+            
+