Instructions to use wesleywt/prot_roberta_mlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wesleywt/prot_roberta_mlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="wesleywt/prot_roberta_mlm")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("wesleywt/prot_roberta_mlm") model = AutoModelForMaskedLM.from_pretrained("wesleywt/prot_roberta_mlm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from wesleywt/prot_roberta_mlm: direct link, hf CLI and curl.
- Browser
- Download file 403 MB
-
https://huggingface.co/wesleywt/prot_roberta_mlm/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://wesleywt/prot_roberta_mlm/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/wesleywt/prot_roberta_mlm/resolve/main/pytorch_model.bin
403 MB
- Xet hash:
- dfa352bfbbdfc2cd6fa5b3199b39b4559576a9c8a3bfcf6a824c24fd4d61afd4
- Size of remote file:
- 403 MB
- SHA256:
- b644c9af93267717ff180d94c3c7c4af7b6e34f0d8166717d734c855fb46edf8
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