Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
json
Sub-tasks:
named-entity-recognition
Languages:
Romanian
Size:
1K - 10K
Tags:
legal
License:
joelniklaus
commited on
Commit
·
8fca89d
1
Parent(s):
09605cb
changed notation scheme to IOB
Browse files- README.md +3 -1
- convert_to_hf_dataset.py +3 -5
- test.jsonl +2 -2
- train.jsonl +2 -2
- validation.jsonl +2 -2
README.md
CHANGED
@@ -3,7 +3,7 @@ annotations_creators:
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- other
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language_creators:
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- found
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-
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- ro
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license:
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- cc-by-nc-nd-4.0
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@@ -89,6 +89,8 @@ The files contain the following data fields
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- `TIME`: Time reference
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- `O`: No entity annotation present
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### Data Splits
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Splits created by Joel Niklaus.
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- other
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language_creators:
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- found
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+
language:
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- ro
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license:
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- cc-by-nc-nd-4.0
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- `TIME`: Time reference
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- `O`: No entity annotation present
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+
The final tagset (in IOB notation) is the following: `['O', 'B-TIME', 'I-TIME', 'B-LEGAL', 'I-LEGAL', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-PER', 'I-PER']`
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+
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### Data Splits
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Splits created by Joel Niklaus.
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convert_to_hf_dataset.py
CHANGED
@@ -1,5 +1,4 @@
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import os
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-
import re
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from glob import glob
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from pathlib import Path
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@@ -17,9 +16,6 @@ base_path = Path("legalnero-data")
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tokenizer = Romanian().tokenizer
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-
# A and D are different government gazettes
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-
# A is the general one, publishing standard legislation, and D is meant for legislation on urban planning and such things
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-
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def process_document(ann_file: str, text_file: Path, metadata: dict, tokenizer) -> List[dict]:
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"""Processes one document (.ann file and .txt file) and returns a list of annotated sentences"""
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# read the ann file into a df
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@@ -55,7 +51,7 @@ def process_document(ann_file: str, text_file: Path, metadata: dict, tokenizer)
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print(f"Could not find entity `{row['entity_text']}` in sentence `{sentence}`")
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ann_sent["words"] = [str(tok) for tok in doc]
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-
ann_sent["ner"] = [tok.ent_type_ if tok.ent_type_ else "O" for tok in doc]
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annotated_sentences.append(ann_sent)
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if not_found_entities > 0:
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@@ -89,6 +85,8 @@ df.ner = df.ner.apply(lambda x: x[:-1])
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# remove rows with containing only one word
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df = df[df.words.map(len) > 1]
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# split by file_name
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num_fn = len(file_names)
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train_fn, validation_fn, test_fn = np.split(np.array(file_names), [int(.8 * num_fn), int(.9 * num_fn)])
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import os
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from glob import glob
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from pathlib import Path
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tokenizer = Romanian().tokenizer
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def process_document(ann_file: str, text_file: Path, metadata: dict, tokenizer) -> List[dict]:
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"""Processes one document (.ann file and .txt file) and returns a list of annotated sentences"""
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# read the ann file into a df
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print(f"Could not find entity `{row['entity_text']}` in sentence `{sentence}`")
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ann_sent["words"] = [str(tok) for tok in doc]
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+
ann_sent["ner"] = [tok.ent_iob_ + "-" + tok.ent_type_ if tok.ent_type_ else "O" for tok in doc]
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annotated_sentences.append(ann_sent)
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if not_found_entities > 0:
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# remove rows with containing only one word
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df = df[df.words.map(len) > 1]
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print(f"The final tagset (in IOB notation) is the following: `{list(df.ner.explode().unique())}`")
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+
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# split by file_name
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num_fn = len(file_names)
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train_fn, validation_fn, test_fn = np.split(np.array(file_names), [int(.8 * num_fn), int(.9 * num_fn)])
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test.jsonl
CHANGED
@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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-
size
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:3c73ba1c9e453754d9efb64a6b94db9ffd0997a36dd4d16a7fcccd449c266513
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size 414576
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train.jsonl
CHANGED
@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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-
oid sha256:
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-
size
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:59f76a7b1c48596c8d3a6b0c19b7c4ff76ca991df091e0c3cb3447ee57386aa7
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size 3301849
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validation.jsonl
CHANGED
@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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-
oid sha256:
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-
size
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:5b81f444ea43588cb3a62968df0d6708aa131754fe7dffee1ba94b5e0757a307
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+
size 426257
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