Instructions to use Jsevisal/bert-gest-pred-seqeval-partialmatch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jsevisal/bert-gest-pred-seqeval-partialmatch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Jsevisal/bert-gest-pred-seqeval-partialmatch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/bert-gest-pred-seqeval-partialmatch") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/bert-gest-pred-seqeval-partialmatch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jsevisal/bert-gest-pred-seqeval-partialmatch: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/Jsevisal/bert-gest-pred-seqeval-partialmatch/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jsevisal/bert-gest-pred-seqeval-partialmatch/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jsevisal/bert-gest-pred-seqeval-partialmatch/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- f8ddcae0f5cab0321ee4bf55b156680ab43bebe764e28e6d532b2b32c04092f9
- Size of remote file:
- 431 MB
- SHA256:
- e37cc4b5caa7bda461d3980188a0ee62e70b65044aa0e551b2e23bb273de5b6d
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