Instructions to use antonthieme/esm2_t30_150M_UR50D_56623590 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use antonthieme/esm2_t30_150M_UR50D_56623590 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="antonthieme/esm2_t30_150M_UR50D_56623590")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("antonthieme/esm2_t30_150M_UR50D_56623590") model = AutoModelForSequenceClassification.from_pretrained("antonthieme/esm2_t30_150M_UR50D_56623590", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b9bdbee49a9b21956ad82df4e3f83ffc987acf5f3e13e15904dfed7b828eccfe
- Size of remote file:
- 5.37 kB
- SHA256:
- 266be24006c75a309f23a14db42f453afa3b19199c3a9387679c8c60e4bfb448
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