Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
100K - 1M
Tags:
named-entity-recognition
ner
token-classification
nlp
natural-language-processing
entity-extraction
License:
Update README.md
Browse files
README.md
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@@ -350,41 +350,6 @@ Tokenize further with `transformers` π€ or `NeuroNER` for model training.
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Visualize the NER tag distribution to understand entity prevalence. Since exact counts are unavailable, the chart below uses estimated data for demonstration. Replace with actual counts after analysis.
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<chartjs>
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{
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"type": "bar",
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"data": {
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"labels": ["O", "B-DATE", "I-DATE", "B-CARDINAL", "B-GPE", "B-ORG", "B-MONEY", "B-PERSON"],
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"datasets": [{
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"label": "NER Tag Counts (Estimated)",
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"data": [100000, 15000, 12000, 10000, 8000, 7000, 5000, 4000],
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"backgroundColor": ["#36A2EB", "#FF6384", "#FFCE56", "#4BC0C0", "#9966FF", "#FF9F40", "#66BB6A", "#EF5350"],
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"borderColor": ["#2A8BBF", "#D9546E", "#D9A83E", "#3A9A9A", "#7A52CC", "#D97F30", "#4CAF50", "#C62828"],
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"borderWidth": 1
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}]
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},
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"options": {
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"plugins": {
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"title": {
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"display": true,
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"text": "CoNLL 2025 NER: Tag Distribution (Estimated)",
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"font": { "size": 16 }
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}
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},
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"scales": {
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"y": {
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"beginAtZero": true,
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"title": { "display": true, "text": "Count" }
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},
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"x": {
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"title": { "display": true, "text": "NER Tag" },
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"ticks": { "autoSkip": false, "maxRotation": 45, "minRotation": 45 }
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}
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}
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}
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}
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</chartjs>
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To compute actual counts:
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```python
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Visualize the NER tag distribution to understand entity prevalence. Since exact counts are unavailable, the chart below uses estimated data for demonstration. Replace with actual counts after analysis.
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To compute actual counts:
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```python
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