Instructions to use LinYuting/bert-base-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LinYuting/bert-base-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="LinYuting/bert-base-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("LinYuting/bert-base-ner") model = AutoModelForTokenClassification.from_pretrained("LinYuting/bert-base-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from LinYuting/bert-base-ner: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/LinYuting/bert-base-ner/resolve/main/tf_model.h5
- Command line
-
hf download hf://LinYuting/bert-base-ner/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/LinYuting/bert-base-ner/resolve/main/tf_model.h5
431 MB
- Xet hash:
- ac4ca5108281b4ed78326db50816e3f671c7e32d857c66baca185b49d48ac20a
- Size of remote file:
- 431 MB
- SHA256:
- 13ab44bd77d466ac930d7e114ecb09d8af622d0901593bd7c7ef618e60354009
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