Transformers
PyTorch
bert
chemistry
smiles
molecular-property-prediction
masked-language-modeling
transfer-learning
model-scaling
Instructions to use sagawa/molscaletransfer-chemlm-4.87m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sagawa/molscaletransfer-chemlm-4.87m with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sagawa/molscaletransfer-chemlm-4.87m", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "encoder_ln_mode": "pre-ln", | |
| "fused_linear_layer": true, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 256, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1024, | |
| "layer_norm_eps": 1e-12, | |
| "layer_norm_type": "apex", | |
| "layernorm_embedding": false, | |
| "max_position_embeddings": 512, | |
| "max_seq_length": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 6, | |
| "pad_token_id": 2, | |
| "position_embedding_type": false, | |
| "sparse_mask_prediction": true, | |
| "task_category": "classification", | |
| "transformers_version": "4.37.2", | |
| "type_vocab_size": 2, | |
| "useLN": true, | |
| "use_cache": true, | |
| "vocab_size": 2368 | |
| } | |