Instructions to use tungpth/BERT_NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tungpth/BERT_NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tungpth/BERT_NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tungpth/BERT_NER") model = AutoModelForTokenClassification.from_pretrained("tungpth/BERT_NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 3, checkpoint
Browse files
last-checkpoint/model.safetensors
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last-checkpoint/optimizer.pt
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last-checkpoint/rng_state.pth
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last-checkpoint/scheduler.pt
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last-checkpoint/trainer_state.json
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"best_metric": 0.
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"eval_samples_per_second": 358.897,
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"eval_steps_per_second": 11.346,
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"logging_steps": 500,
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"attributes": {}
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"learning_rate": 9.698463103929542e-06,
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{
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"epoch": 3.0,
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"eval_accuracy": 0.9859576186274657,
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"eval_loss": 0.05013062804937363,
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