Instructions to use Jinchen/bert-base-uncased-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jinchen/bert-base-uncased-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Jinchen/bert-base-uncased-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jinchen/bert-base-uncased-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("Jinchen/bert-base-uncased-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jinchen/bert-base-uncased-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 218 MB
-
https://huggingface.co/Jinchen/bert-base-uncased-finetuned-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jinchen/bert-base-uncased-finetuned-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jinchen/bert-base-uncased-finetuned-ner/resolve/main/pytorch_model.bin
218 MB
- Xet hash:
- 521c4fc7ae2948dbecb0f76ab421f788502f4ce4dc77d4b289bed50cd776ad9b
- Size of remote file:
- 218 MB
- SHA256:
- e3d568ae61d733805ff1ab89646065336d664025b39dfabcfea6936c577e2816
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