Instructions to use UGARIT/grc-ner-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UGARIT/grc-ner-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="UGARIT/grc-ner-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("UGARIT/grc-ner-bert") model = AutoModelForTokenClassification.from_pretrained("UGARIT/grc-ner-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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Pretrained NER tagging model for ancient Greek
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# Usage
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```python
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Pretrained NER tagging model for ancient Greek
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# Data
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### Training Dataset
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| | **Person** | **Location** | **NORP** |
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|----------------|------------------|-------------------|-------------------|
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| Odyssey | 2.469 | 698 | 0 |
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| Deipnosophists | 14.921 | 2.699 | 5.110 |
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| Pausanias | 10.205 | 8.670 | 4.972 |
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| Other Datasets | 3.283 | 2.040 | 1.089 |
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| **Total** | **30.878** | **14.107** | **11.171** |
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### Validation Dataset
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| | **Person** | **Location** | **NORP** |
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|----------------|------------------|-------------------|-------------------|
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| Xenophon | 1.190 | 796 | 857 |
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# Usage
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```python
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