Instructions to use jsm33/d4-primary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jsm33/d4-primary with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, RobertaForSequenceClassificationWithLayeredSentiment tokenizer = AutoTokenizer.from_pretrained("jsm33/d4-primary") model = RobertaForSequenceClassificationWithLayeredSentiment.from_pretrained("jsm33/d4-primary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jsm33/d4-primary: direct link, hf CLI and curl.
- Browser
- Download file 547 MB
-
https://huggingface.co/jsm33/d4-primary/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jsm33/d4-primary/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jsm33/d4-primary/resolve/main/pytorch_model.bin
547 MB
- Xet hash:
- a7495520ddf4653165e2481c54b5de3bf48312b3a2ec0ebd9e51431eaa8fbd6a
- Size of remote file:
- 547 MB
- SHA256:
- 633e11fd8dd78812065fa3286077f09b0b48e9b8b695406e589062cfeaad1f8f
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