Instructions to use multimolecule/mmsplice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/mmsplice with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/mmsplice") model = AutoModel.from_pretrained("multimolecule/mmsplice") - Notebooks
- Google Colab
- Kaggle
| { | |
| "acceptor_exon_length": 3, | |
| "acceptor_intron_cut": 6, | |
| "acceptor_intron_length": 50, | |
| "architectures": [ | |
| "MMSpliceForSequencePrediction" | |
| ], | |
| "bos_token_id": null, | |
| "donor_exon_length": 5, | |
| "donor_intron_cut": 6, | |
| "donor_intron_length": 13, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "head": { | |
| "act": null, | |
| "bias": true, | |
| "dropout": 0.0, | |
| "hidden_size": 1, | |
| "layer_norm_eps": 1e-12, | |
| "loss_weight": null, | |
| "num_labels": 1, | |
| "output_name": null, | |
| "problem_type": "regression", | |
| "transform": null, | |
| "transform_act": "gelu", | |
| "type": null | |
| }, | |
| "hidden_size": 5, | |
| "id2label": null, | |
| "label2id": null, | |
| "mask_token_id": 4, | |
| "model_type": "mmsplice", | |
| "modules_config": { | |
| "acceptor": { | |
| "architecture": "dense", | |
| "batch_norm_eps": 0.001, | |
| "conv_activation": "relu", | |
| "conv_batch_norm": true, | |
| "conv_channels": 32, | |
| "conv_kernel_size": 15, | |
| "dropout": 0.2, | |
| "flatten_dropout": true, | |
| "hidden_sizes": [], | |
| "pointwise_channels": 32, | |
| "pool_mask_zeros": false, | |
| "region_length": 53 | |
| }, | |
| "acceptor_intron": { | |
| "architecture": "conv", | |
| "batch_norm_eps": 0.001, | |
| "conv_activation": "relu", | |
| "conv_batch_norm": false, | |
| "conv_channels": 256, | |
| "conv_kernel_size": 13, | |
| "dropout": 0.2, | |
| "flatten_dropout": false, | |
| "hidden_sizes": [], | |
| "pointwise_channels": 0, | |
| "pool_mask_zeros": false, | |
| "region_length": 0 | |
| }, | |
| "donor": { | |
| "architecture": "dense", | |
| "batch_norm_eps": 0.001, | |
| "conv_activation": "linear", | |
| "conv_batch_norm": false, | |
| "conv_channels": 0, | |
| "conv_kernel_size": 0, | |
| "dropout": 0.2, | |
| "flatten_dropout": false, | |
| "hidden_sizes": [ | |
| 128, | |
| 64 | |
| ], | |
| "pointwise_channels": 0, | |
| "pool_mask_zeros": false, | |
| "region_length": 18 | |
| }, | |
| "donor_intron": { | |
| "architecture": "conv", | |
| "batch_norm_eps": 0.001, | |
| "conv_activation": "relu", | |
| "conv_batch_norm": false, | |
| "conv_channels": 256, | |
| "conv_kernel_size": 13, | |
| "dropout": 0.2, | |
| "flatten_dropout": false, | |
| "hidden_sizes": [], | |
| "pointwise_channels": 0, | |
| "pool_mask_zeros": false, | |
| "region_length": 0 | |
| }, | |
| "exon": { | |
| "architecture": "conv", | |
| "batch_norm_eps": 0.001, | |
| "conv_activation": "relu", | |
| "conv_batch_norm": true, | |
| "conv_channels": 128, | |
| "conv_kernel_size": 11, | |
| "dropout": 0.2, | |
| "flatten_dropout": false, | |
| "hidden_sizes": [], | |
| "pointwise_channels": 0, | |
| "pool_mask_zeros": true, | |
| "region_length": 0 | |
| } | |
| }, | |
| "null_token_id": 5, | |
| "num_labels": 1, | |
| "pad_token_id": 4, | |
| "problem_type": "regression", | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.7.0", | |
| "unk_token_id": 3, | |
| "vocab_size": 4 | |
| } | |