Instructions to use jcbao77/bert-uncased-multi-task-dynamic-weights-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcbao77/bert-uncased-multi-task-dynamic-weights-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jcbao77/bert-uncased-multi-task-dynamic-weights-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jcbao77/bert-uncased-multi-task-dynamic-weights-ner") model = AutoModelForTokenClassification.from_pretrained("jcbao77/bert-uncased-multi-task-dynamic-weights-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jcbao77/bert-uncased-multi-task-dynamic-weights-ner: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/jcbao77/bert-uncased-multi-task-dynamic-weights-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jcbao77/bert-uncased-multi-task-dynamic-weights-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jcbao77/bert-uncased-multi-task-dynamic-weights-ner/resolve/main/pytorch_model.bin
436 MB
- Xet hash:
- 60d91f929294987db85368267ee45cd3c18a1f870876617ffa9f0bd0881c7472
- Size of remote file:
- 436 MB
- SHA256:
- 7a5bdec98aabad52220817ec475ad6f35c90d04437501d3033ccf2709935077a
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