Instructions to use gabrielbianchin/base_esm2_t12_long with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gabrielbianchin/base_esm2_t12_long with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gabrielbianchin/base_esm2_t12_long")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gabrielbianchin/base_esm2_t12_long") model = AutoModelForMaskedLM.from_pretrained("gabrielbianchin/base_esm2_t12_long", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7caf12bde60f9253937123762df339d7468361cc64793665e5da1527e4d94c82
- Size of remote file:
- 171 MB
- SHA256:
- 4970d486963dfa0bb137d0265ec90a3eb6154e2d5e95fddb52f397b034ab3f4f
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