Instructions to use multimolecule/ribonanzanet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/ribonanzanet with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/ribonanzanet") model = AutoModel.from_pretrained("multimolecule/ribonanzanet") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "RibonanzaNetForPreTraining" | |
| ], | |
| "attention_dropout": 0.05, | |
| "bos_token_id": 1, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "fix_attention_mask": false, | |
| "fix_attention_residual": false, | |
| "fix_pairwise_dropout": false, | |
| "head": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout": 0.05, | |
| "hidden_size": 256, | |
| "id2label": null, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1024, | |
| "kernel_size": 5, | |
| "label2id": null, | |
| "layer_norm_eps": 1e-12, | |
| "mask_token_id": 4, | |
| "model_type": "ribonanzanet", | |
| "null_token_id": 5, | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 9, | |
| "num_labels": 1, | |
| "output_pairwise_states": false, | |
| "pad_token_id": 0, | |
| "pairwise_attention_size": 32, | |
| "pairwise_hidden_act": "relu", | |
| "pairwise_intermediate_size": 256, | |
| "pairwise_num_attention_heads": 4, | |
| "pairwise_size": 64, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "5.9.0", | |
| "type_vocab_size": 2, | |
| "unk_token_id": 3, | |
| "use_triangular_attention": false, | |
| "vocab_size": 28 | |
| } | |