Instructions to use multimolecule/deepmel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/deepmel with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/deepmel") model = AutoModel.from_pretrained("multimolecule/deepmel") inputs = tokenizer("ACTCCCCTGCCCTCAACAAGATGTTTTGCCAACTGGCCAAGACCTGCCCTGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACATGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATGG", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_state - Notebooks
- Google Colab
- Kaggle
File size: 1,032 Bytes
010e2db | 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 39 40 41 42 43 44 45 46 47 48 | {
"architectures": [
"DeepMelForSequencePrediction"
],
"bos_token_id": 1,
"conv_channels": 128,
"conv_dropout": 0.2,
"conv_kernel_size": 20,
"dtype": "float32",
"eos_token_id": 2,
"fc_dim": 256,
"fc_dropout": 0.4,
"head": {
"act": null,
"bias": true,
"dropout": 0.0,
"hidden_size": 256,
"layer_norm_eps": 1e-12,
"loss_weight": null,
"num_labels": 24,
"output_name": null,
"problem_type": "multilabel",
"transform": null,
"transform_act": "gelu",
"type": null
},
"hidden_act": "relu",
"hidden_size": 256,
"id2label": null,
"input_length": 500,
"label2id": null,
"lstm_dropout": 0.1,
"lstm_hidden_size": 128,
"lstm_recurrent_dropout": 0.1,
"mask_token_id": 4,
"model_type": "deepmel",
"null_token_id": 5,
"num_labels": 24,
"pad_token_id": 0,
"pool_size": 10,
"recurrent_dropout": 0.2,
"tie_word_embeddings": true,
"time_distributed_channels": 128,
"transformers_version": "5.9.0",
"unk_token_id": 3,
"vocab_size": 5
}
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