Instructions to use multimolecule/rnabert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/rnabert with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/rnabert") model = AutoModel.from_pretrained("multimolecule/rnabert") inputs = tokenizer("UAGCUUAUCAGACUGAUGUUGA", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_stateimport multimolecule from transformers import pipeline predictor = pipeline("fill-mask", model="multimolecule/rnabert") output = predictor("UAGCUUAUCAG<mask>CUGAUGUUGA") - Notebooks
- Google Colab
- Kaggle
File size: 849 Bytes
397ff6a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | {
"add_cross_attention": false,
"architectures": [
"RnaBertForPreTraining"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "float32",
"eos_token_id": 2,
"head": null,
"hidden_act": "gelu",
"hidden_dropout": 0.0,
"hidden_size": 120,
"id2label": null,
"initializer_range": 0.02,
"intermediate_size": 40,
"is_decoder": false,
"label2id": null,
"layer_norm_eps": 1e-12,
"lm_head": null,
"mask_token_id": 4,
"max_position_embeddings": 440,
"model_type": "rnabert",
"null_token_id": 5,
"num_attention_heads": 12,
"num_hidden_layers": 6,
"num_labels": 1,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"ss_vocab_size": 8,
"tie_word_embeddings": false,
"transformers_version": "5.9.0",
"type_vocab_size": 2,
"unk_token_id": 3,
"use_cache": true,
"vocab_size": 28
}
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