Instructions to use multimolecule/deepcpgdna-hou2016-hepg2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/deepcpgdna-hou2016-hepg2 with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/deepcpgdna-hou2016-hepg2") model = AutoModel.from_pretrained("multimolecule/deepcpgdna-hou2016-hepg2") inputs = tokenizer("ACTCCCCTGCCCTCAACAAGATGTTTTGCCAACTGGCCAAGACCTGCCCTGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACATGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATGG", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_state - Notebooks
- Google Colab
- Kaggle
File size: 1,177 Bytes
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"architectures": [
"DeepCpgDnaForSequencePrediction"
],
"bos_token_id": 1,
"bottleneck_size": 128,
"conv_channels": [
128,
256,
512
],
"conv_kernel_sizes": [
11,
3,
3
],
"conv_pool_sizes": [
4,
2,
2
],
"dtype": "float32",
"eos_token_id": 2,
"head": {
"act": null,
"bias": true,
"dropout": 0.0,
"hidden_size": 128,
"layer_norm_eps": 1e-12,
"loss_weight": null,
"num_labels": 6,
"output_name": null,
"problem_type": "binary",
"transform": null,
"transform_act": "gelu",
"type": null
},
"hidden_act": "relu",
"hidden_dropout": 0.0,
"hidden_size": 128,
"id2label": {
"0": "HepG21",
"1": "HepG22",
"2": "HepG23",
"3": "HepG24",
"4": "HepG25",
"5": "HepG26"
},
"label2id": {
"HepG21": 0,
"HepG22": 1,
"HepG23": 2,
"HepG24": 3,
"HepG25": 4,
"HepG26": 5
},
"mask_token_id": 4,
"model_type": "deepcpgdna",
"null_token_id": 5,
"num_labels": 6,
"pad_token_id": 0,
"sequence_length": 1001,
"tie_word_embeddings": true,
"transformers_version": "5.9.0",
"unk_token_id": 3,
"vocab_size": 5
}
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