Instructions to use CompBioDSA/MutBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CompBioDSA/MutBERT with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CompBioDSA/MutBERT", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { "_name_or_path": "JadenLong/MutBERT", | |
| "auto_map": { | |
| "AutoConfig": "modeling_mutbert.RoPEBertConfig", | |
| "AutoModel": "modeling_mutbert.RoPEBertModel", | |
| "AutoModelForMaskedLM": "modeling_mutbert.RoPEBertForMaskedLM", | |
| "AutoModelForSequenceClassification": "modeling_mutbert.RoPEBertForSequenceClassification" | |
| }, | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 3, | |
| "pooler_type": "mean", | |
| "rope_scaling": null, | |
| "rope_theta": 10000.0, | |
| "transformers_version": "4.45.2", | |
| "type_vocab_size": 2, | |
| "vocab_size": 9 | |
| } | |