Instructions to use multimolecule/scbasset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/scbasset with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/scbasset") model = AutoModel.from_pretrained("multimolecule/scbasset") inputs = tokenizer("ACTCCCCTGCCCTCAACAAGATGTTTTGCCAACTGGCCAAGACCTGCCCTGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACATGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATGG", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_state - Notebooks
- Google Colab
- Kaggle
File size: 1,104 Bytes
ce1cc2b | 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 49 50 51 52 53 54 55 | {
"architectures": [
"ScBassetForSequencePrediction"
],
"batch_norm_eps": 0.001,
"batch_norm_momentum": 0.1,
"bos_token_id": 1,
"bottleneck_size": 32,
"dtype": "float32",
"eos_token_id": 2,
"head": {
"act": null,
"bias": true,
"dropout": 0.0,
"hidden_size": 32,
"layer_norm_eps": 1e-12,
"loss_weight": null,
"num_labels": 2034,
"output_name": null,
"problem_type": "binary",
"transform": null,
"transform_act": "gelu",
"type": null
},
"hidden_act": "quick_gelu",
"hidden_dropout": 0.2,
"hidden_size": 32,
"id2label": null,
"label2id": null,
"mask_token_id": 4,
"model_type": "scbasset",
"null_token_id": 5,
"num_labels": 2034,
"pad_token_id": 0,
"pointwise_channels": 256,
"sequence_length": 1344,
"stem_channels": 288,
"stem_kernel_size": 17,
"stem_pool_size": 3,
"tie_word_embeddings": true,
"tower_channels": [
288,
323,
363,
407,
456,
512
],
"tower_kernel_size": 5,
"tower_pool_size": 2,
"transformers_version": "5.9.0",
"unk_token_id": 3,
"vocab_size": 5
}
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