Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use redevaaa/test4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use redevaaa/test4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="redevaaa/test4")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("redevaaa/test4") model = AutoModelForTokenClassification.from_pretrained("redevaaa/test4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d971edc8b34bb78a9502d2215d0b4c8c72066eaea3c1e22e433c7142f9fea757
- Size of remote file:
- 431 MB
- SHA256:
- 9c02268b7b9ff126a9a64547e7ae3b26d6db2a8b6bf40017c6d2b0c3007123e5
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