Fill-Mask
Transformers
Safetensors
English
esmc
biology
esm
protein
protein-language-model
protein-embeddings
masked-language-modeling
transfer-learning
variant-effect-prediction
protein-engineering
custom_code
Instructions to use biohub/ESMC-300M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-300M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="biohub/ESMC-300M", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("biohub/ESMC-300M", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("biohub/ESMC-300M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- config.json +1 -1
config.json
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"d_model": 960,
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"dtype": "float32",
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"initializer_range": 0.02,
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"mask_token_id":
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"model_type": "esmc",
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"n_heads": 15,
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"n_layers": 30,
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"d_model": 960,
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"dtype": "float32",
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"initializer_range": 0.02,
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"mask_token_id": 32,
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"model_type": "esmc",
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"n_heads": 15,
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"n_layers": 30,
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