Instructions to use multimolecule/basset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/basset with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/basset") model = AutoModel.from_pretrained("multimolecule/basset") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BassetForSequencePrediction" | |
| ], | |
| "batch_norm_eps": 1e-05, | |
| "batch_norm_momentum": 0.1, | |
| "bos_token_id": 1, | |
| "conv_channels": [ | |
| 300, | |
| 200, | |
| 200 | |
| ], | |
| "conv_kernel_sizes": [ | |
| 19, | |
| 11, | |
| 7 | |
| ], | |
| "conv_pool_sizes": [ | |
| 3, | |
| 4, | |
| 4 | |
| ], | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "fc_sizes": [ | |
| 1000, | |
| 1000 | |
| ], | |
| "head": { | |
| "act": null, | |
| "bias": true, | |
| "dropout": 0.0, | |
| "hidden_size": 1000, | |
| "layer_norm_eps": 1e-12, | |
| "loss_weight": null, | |
| "num_labels": 164, | |
| "output_name": null, | |
| "problem_type": "multilabel", | |
| "transform": null, | |
| "transform_act": "gelu", | |
| "type": null | |
| }, | |
| "hidden_act": "relu", | |
| "hidden_dropout": 0.3, | |
| "hidden_size": 1000, | |
| "id2label": null, | |
| "label2id": null, | |
| "mask_token_id": 4, | |
| "model_type": "basset", | |
| "null_token_id": 5, | |
| "num_conv_layers": 3, | |
| "num_labels": 164, | |
| "pad_token_id": 0, | |
| "sequence_length": 600, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.7.0", | |
| "unk_token_id": 3, | |
| "vocab_size": 4 | |
| } | |