Instructions to use lauraha/esm_mhc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lauraha/esm_mhc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lauraha/esm_mhc")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lauraha/esm_mhc") model = AutoModelForMaskedLM.from_pretrained("lauraha/esm_mhc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ac93751b60e02a841d111fc12914b49e08f2fb58b59ff9c6578a2ade509b673c
- Size of remote file:
- 5.18 kB
- SHA256:
- 67e7a639059bda321e8488610494654dc3045c8b9647090dd011bb738c833967
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