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Datasets:
Kriyans
/
ner

Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K<n<10K
License:
Dataset card Files Community
4
Kriyans commited on
Commit
6217f1a
•
1 Parent(s): 58dd593

Update indian_names.py

Browse files
Files changed (1) hide show
  1. indian_names.py +76 -76
indian_names.py CHANGED
@@ -58,84 +58,84 @@ class indian_names(datasets.GeneratorBasedBuilder):
58
  datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
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  ]
60
 
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- def _generate_examples(self, filepath):
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- logger.info("⏳ Generating examples from = %s", filepath)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  with open(filepath, encoding="utf-8") as f:
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- current_tokens = []
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- current_labels = []
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- sentence_counter = 0
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- for row in f:
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- row = row.rstrip()
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- if row:
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- # Check if the delimiter ("\t") is present in the row
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- if "\t" in row:
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  token, label = row.split("\t")
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  current_tokens.append(token)
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  current_labels.append(label)
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  else:
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- # Handle cases where the delimiter is missing
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- # You can choose to skip these rows or handle them differently
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- logger.warning(f"Delimiter missing in row: {row}")
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- else:
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- # New sentence
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- if not current_tokens:
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- # Consecutive empty lines will cause empty sentences
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- continue
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- assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
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- sentence = (
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- sentence_counter,
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- {
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- "id": str(sentence_counter),
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- "tokens": current_tokens,
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- "ner_tags": current_labels,
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- },
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- )
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- sentence_counter += 1
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- current_tokens = []
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- current_labels = []
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- yield sentence
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- # Don't forget the last sentence in the dataset 🧐
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- if current_tokens:
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- yield sentence_counter, {
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- "id": str(sentence_counter),
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- "tokens": current_tokens,
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- "ner_tags": current_labels,
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- }
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-
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- # def _generate_examples(self, filepath):
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- # logger.info("⏳ Generating examples from = %s", filepath)
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- # with open(filepath, encoding="utf-8") as f:
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- # current_tokens = []
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- # current_labels = []
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- # sentence_counter = 0
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- # for row in f:
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- # row = row.rstrip()
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- # if row:
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- # token, label = row.split("\t")
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- # current_tokens.append(token)
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- # current_labels.append(label)
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- # else:
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- # # New sentence
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- # if not current_tokens:
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- # # Consecutive empty lines will cause empty sentences
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- # continue
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- # assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
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- # sentence = (
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- # sentence_counter,
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- # {
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- # "id": str(sentence_counter),
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- # "tokens": current_tokens,
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- # "ner_tags": current_labels,
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- # },
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- # )
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- # sentence_counter += 1
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- # current_tokens = []
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- # current_labels = []
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- # yield sentence
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- # # Don't forget last sentence in dataset 🧐
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- # if current_tokens:
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- # yield sentence_counter, {
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- # "id": str(sentence_counter),
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- # "tokens": current_tokens,
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- # "ner_tags": current_labels,
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- # }
 
58
  datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
59
  ]
60
 
61
+ # def _generate_examples(self, filepath):
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+ # logger.info("⏳ Generating examples from = %s", filepath)
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+ # with open(filepath, encoding="utf-8") as f:
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+ # current_tokens = []
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+ # current_labels = []
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+ # sentence_counter = 0
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+ # for row in f:
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+ # row = row.rstrip()
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+ # if row:
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+ # # Check if the delimiter ("\t") is present in the row
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+ # if "\t" in row:
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+ # token, label = row.split("\t")
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+ # current_tokens.append(token)
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+ # current_labels.append(label)
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+ # else:
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+ # # Handle cases where the delimiter is missing
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+ # # You can choose to skip these rows or handle them differently
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+ # logger.warning(f"Delimiter missing in row: {row}")
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+ # else:
80
+ # # New sentence
81
+ # if not current_tokens:
82
+ # # Consecutive empty lines will cause empty sentences
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+ # continue
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+ # assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
85
+ # sentence = (
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+ # sentence_counter,
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+ # {
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+ # "id": str(sentence_counter),
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+ # "tokens": current_tokens,
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+ # "ner_tags": current_labels,
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+ # },
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+ # )
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+ # sentence_counter += 1
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+ # current_tokens = []
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+ # current_labels = []
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+ # yield sentence
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+ # # Don't forget the last sentence in the dataset 🧐
98
+ # if current_tokens:
99
+ # yield sentence_counter, {
100
+ # "id": str(sentence_counter),
101
+ # "tokens": current_tokens,
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+ # "ner_tags": current_labels,
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+ # }
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+
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+ def _generate_examples(self, filepath):
106
+ logger.info("⏳ Generating examples from = %s", filepath)
107
  with open(filepath, encoding="utf-8") as f:
108
+ current_tokens = []
109
+ current_labels = []
110
+ sentence_counter = 0
111
+ for row in f:
112
+ row = row.rstrip()
113
+ if row:
 
 
114
  token, label = row.split("\t")
115
  current_tokens.append(token)
116
  current_labels.append(label)
117
  else:
118
+ # New sentence
119
+ if not current_tokens:
120
+ # Consecutive empty lines will cause empty sentences
121
+ continue
122
+ assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
123
+ sentence = (
124
+ sentence_counter,
125
+ {
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+ "id": str(sentence_counter),
127
+ "tokens": current_tokens,
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+ "ner_tags": current_labels,
129
+ },
130
+ )
131
+ sentence_counter += 1
132
+ current_tokens = []
133
+ current_labels = []
134
+ yield sentence
135
+ # Don't forget last sentence in dataset 🧐
136
+ if current_tokens:
137
+ yield sentence_counter, {
138
+ "id": str(sentence_counter),
139
+ "tokens": current_tokens,
140
+ "ner_tags": current_labels,
141
+ }