Instructions to use eporetsky/esm2_t12_35M_UR50D-finetuned-localization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eporetsky/esm2_t12_35M_UR50D-finetuned-localization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eporetsky/esm2_t12_35M_UR50D-finetuned-localization")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eporetsky/esm2_t12_35M_UR50D-finetuned-localization") model = AutoModelForSequenceClassification.from_pretrained("eporetsky/esm2_t12_35M_UR50D-finetuned-localization") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c7690a1e285017758a98bf8d341d827b2d4b356be42c0c8bc410965d1ff47cf
- Size of remote file:
- 3.45 kB
- SHA256:
- c6546a9dc147b6e71449909ff5b4be59db63be16adebfdddad8a2ad7daf16e34
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.