Instructions to use Jsevisal/distilbert-base-gest-pred-seqeval-partialmatch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jsevisal/distilbert-base-gest-pred-seqeval-partialmatch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Jsevisal/distilbert-base-gest-pred-seqeval-partialmatch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/distilbert-base-gest-pred-seqeval-partialmatch") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/distilbert-base-gest-pred-seqeval-partialmatch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jsevisal/distilbert-base-gest-pred-seqeval-partialmatch: direct link, hf CLI and curl.
- Browser
- Download file 261 MB
-
https://huggingface.co/Jsevisal/distilbert-base-gest-pred-seqeval-partialmatch/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jsevisal/distilbert-base-gest-pred-seqeval-partialmatch/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/Jsevisal/distilbert-base-gest-pred-seqeval-partialmatch/resolve/main/pytorch_model.bin
261 MB
- Xet hash:
- 5f20e12f41a60510c06565adcabb5d28636ad3e08fa397f09c9d5743a16c1dfd
- Size of remote file:
- 261 MB
- SHA256:
- 3172dce29538699e2221a50a2869b805f77404aad02e95630a546526036efed6
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