Instructions to use antonthieme/esm2_t6_8M_UR50D_56621481 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use antonthieme/esm2_t6_8M_UR50D_56621481 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="antonthieme/esm2_t6_8M_UR50D_56621481")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("antonthieme/esm2_t6_8M_UR50D_56621481") model = AutoModelForSequenceClassification.from_pretrained("antonthieme/esm2_t6_8M_UR50D_56621481", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b7b48077d739c3433d06bb503660ea8bfecbbf78cca49ec69b736ad7297569fb
- Size of remote file:
- 31.4 MB
- SHA256:
- 2ac2ea0679fb3178694c56da52e31ab5e12705cabde96e7e32ff3ceaef8f408a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.