Instructions to use antonthieme/esm2_t12_35M_UR50D_56623563 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use antonthieme/esm2_t12_35M_UR50D_56623563 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="antonthieme/esm2_t12_35M_UR50D_56623563")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("antonthieme/esm2_t12_35M_UR50D_56623563") model = AutoModelForSequenceClassification.from_pretrained("antonthieme/esm2_t12_35M_UR50D_56623563", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3d4e3efeb2daf07476a34b8b09bb9c9cc96f487130efb31086ee36d9ded26e9
- Size of remote file:
- 136 MB
- SHA256:
- 76e971fd33cd779e19e69ba89ed4987ac5397e0fd33470d33fec9ba07dff7bcd
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