Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use awilli/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use awilli/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="awilli/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("awilli/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("awilli/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from awilli/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/awilli/bert-finetuned-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://awilli/bert-finetuned-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/awilli/bert-finetuned-ner/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 902e8bf3938921f6b373b5b4a282253e09410241b1b26e47ae9a656a332c375a
- Size of remote file:
- 431 MB
- SHA256:
- d18a95f5517e67086ca8568b8cb8846a2c9024528b1e0e9f15c5c3155b231674
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