Instructions to use Jsevisal/roberta-large-gest-pred-seqeval-partialmatch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jsevisal/roberta-large-gest-pred-seqeval-partialmatch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Jsevisal/roberta-large-gest-pred-seqeval-partialmatch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/roberta-large-gest-pred-seqeval-partialmatch") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/roberta-large-gest-pred-seqeval-partialmatch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jsevisal/roberta-large-gest-pred-seqeval-partialmatch: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/Jsevisal/roberta-large-gest-pred-seqeval-partialmatch/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jsevisal/roberta-large-gest-pred-seqeval-partialmatch/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/Jsevisal/roberta-large-gest-pred-seqeval-partialmatch/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- 9d0f11659004cb21cc672584fda6bdc08ef6e5cf58617e3d8cceccbf21711b80
- Size of remote file:
- 1.42 GB
- SHA256:
- 08ed5e6cd35449e996554f18cf6f3a03d517cb0f5839bb5373c2ca2f198c8e40
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