Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
chemistry
biology
medical
Instructions to use javicorvi/pretoxtm-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use javicorvi/pretoxtm-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="javicorvi/pretoxtm-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("javicorvi/pretoxtm-ner") model = AutoModelForTokenClassification.from_pretrained("javicorvi/pretoxtm-ner") - Notebooks
- Google Colab
- Kaggle
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README.md
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# pretoxtm-ner
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This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on [javicorvi/pretoxtm-dataset]
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It achieves the following results on the evaluation set:
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- Loss: 0.2722
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- Study Test: {'precision': 0.8222222222222222, 'recall': 0.8763157894736842, 'f1': 0.8484076433121018, 'number': 380}
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# pretoxtm-ner
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This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on [javicorvi/pretoxtm-dataset](https://huggingface.co/datasets/javicorvi/pretoxtm-dataset).
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It achieves the following results on the evaluation set:
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- Loss: 0.2722
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- Study Test: {'precision': 0.8222222222222222, 'recall': 0.8763157894736842, 'f1': 0.8484076433121018, 'number': 380}
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