Upload NER-BAILII-UK-CCA.py

NER-BAILII-UK-CCA.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
+"""Waleed-bin-Qamar/NER-BAILII-UK-CCA. Criminal court appeals of uk"""
+
+import csv
+import json
+import os
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@inproceedings{title = "A great new dataset",
+author = "A great new dataset",
+booktitle = "A great new dataset",
+month = sep,
+year = "2050",
+address = "a,b",
+publisher = "Association in b",
+doi = " ",
+pages = " ",
+abstract = " ",
+}
+"""
+
+_DESCRIPTION = """\
+A great new dataset
+"""
+# due privacy issues private addresses are used
+_URL = "https://drive.google.com/"
+_TRAINING_FILE_URL = "uc?id=1Hn30-V5tm_JxKB7Q09BpZJu1VylinpGu&export=download"
+_DEV_FILE_URL = "uc?id=1saGAPzk0zaj6dIo5FKa1rb5SCdHwJnAq&export=download"
+_TEST_FILE_URL = "uc?id=1stsN0Iq1guKNH0oJc0ZjqIj57ExR_iYT&export=download"
+
+
+class NER_BAILIIConfig(datasets.BuilderConfig):
+  def __init__(self, **kwargs):
+    """BuilderConfig for NER_BAILIIConfig.
+    
+    Args:
+    **kwargs: keyword arguments forwarded to super.
+    """
+    super(NER_BAILIIConfig, self).__init__(**kwargs)
+
+
+class NER_BAILII(datasets.GeneratorBasedBuilder):
+  """NER-BAILII-UK-CCA. Criminal court appeals of uk"""
+  BUILDER_CONFIGS = [
+      NER_BAILIIConfig(
+          name="NER-BAILII-UK-CCA", version=datasets.Version("1.0.0"), description="The NER-BAILII-UK-CCA Name Entities recognization 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-defendant_pleaded_guilty',
+                         'I-defendant_pleaded_guilty',
+                         'B-appeal_against_sentence',
+                         'I-appeal_against_sentence',
+                         'B-defendant',
+                         'I-defendant',
+                         'B-date_of_original_trial_or_conviction',
+                         'I-date_of_original_trial_or_conviction',
+                         'B-offence',
+                         'I-offence',
+                         'B-defendant_original_sentence',
+                         'I-defendant_original_sentence',
+                         'B-decision_granted',
+                         'I-decision_granted',
+                         'B-date_of_crime',
+                         'I-date_of_crime',
+                         'B-decision_refused',
+                         'I-decision_refused',
+                         'B-expert_witness_used_at_original_trail',
+                         'I-expert_witness_used_at_original_trail',
+                         'B-victim_age',
+                         'I-victim_age',
+                         'B-defendant_is_female',
+                         'I-defendant_is_female',
+                         'B-appeal_against_conviction',
+                         'I-appeal_against_conviction',
+                         'B-defendant_custodial_sentence_years',
+                         'I-defendant_custodial_sentence_years',
+                         'B-date_of_offence',
+                         'I-date_of_offence',
+                         'B-decision_quashed',
+                         'I-decision_quashed',
+                         'B-defendant_gender',
+                         'B-victim_is_male',
+                         'B-victim_is_female',                                
+                         'B-victim_gender',
+                         'B-previous_convictions',
+                         'I-previous_convictions',
+                         'I-s_age',
+                     ]
+                 )
+             )
+             
+            }
+            
+        ),
+        supervised_keys=None,
+        homepage="http://linkedin",
+        citation=_CITATION,
+    )
+
+  def _split_generators(self, dl_manager):
+    """Returns SplitGenerators."""
+    #_TEST_FILE_URL
+    #_DEV_FILE_URL
+    #_TRAINING_FILE_URL
+    urls_to_download = {
+        "train": f"{_URL}{_TRAINING_FILE_URL}",
+        "dev": f"{_URL}{_DEV_FILE_URL}",
+        "test": f"{_URL}{_TEST_FILE_URL}",
+    }
+    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["dev"]}),
+        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:
+      current_tokens = []
+      current_labels = []
+      sentence_counter = 0
+
+      for row in f:
+        row = row.rstrip()
+        if row:
+          token, label = row.split("\t")
+          current_tokens.append(token)
+          current_labels.append(label)
+        else:
+          # New sentence
+          if not current_tokens:
+            # Consecutive empty lines will cause empty sentences
+            continue
+          assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
+          sentence = (
+              sentence_counter,
+              {
+                  "id": str(sentence_counter),
+                  "tokens": current_tokens,
+                  "ner_tags": current_labels,
+              },
+          )
+          sentence_counter += 1
+          current_tokens = []
+          current_labels = []
+          yield sentence
+          
+    # Don't forget last sentence in dataset 🧐
+    if current_tokens:
+      yield sentence_counter, {
+          "id": str(sentence_counter),
+          "tokens": current_tokens,
+          "ner_tags": current_labels,
+      }