Instructions to use b3x0m/bert-xomlac-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use b3x0m/bert-xomlac-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="b3x0m/bert-xomlac-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("b3x0m/bert-xomlac-ner") model = AutoModelForTokenClassification.from_pretrained("b3x0m/bert-xomlac-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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by trungdang2901 - opened
README.md
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- accuracy
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pipeline_tag: token-classification
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Too lazy to write something
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- accuracy
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pipeline_tag: token-classification
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Too lazy to write something
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Fine-tuned from bert-base-uncased with my own dataset.
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val_loss = 0.07 | val_acc = 0.89 | f-1 score = 0.73
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