Instructions to use multimolecule/bpnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/bpnet with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/bpnet") model = AutoModel.from_pretrained("multimolecule/bpnet") - Notebooks
- Google Colab
- Kaggle
File size: 891 Bytes
fca42c1 | 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 | {
"architectures": [
"BPNetForProfilePrediction"
],
"bos_token_id": null,
"count_loss_weight": 1.0,
"dilated_kernel_size": 3,
"dtype": "float32",
"eos_token_id": null,
"head": {
"act": null,
"bias": true,
"dropout": 0.0,
"hidden_size": null,
"layer_norm_eps": 1e-12,
"loss_weight": null,
"num_labels": null,
"output_name": null,
"problem_type": "regression",
"transform": null,
"transform_act": "gelu",
"type": null
},
"hidden_act": "relu",
"hidden_size": 64,
"id2label": null,
"label2id": null,
"mask_token_id": 4,
"model_type": "bpnet",
"null_token_id": 5,
"num_dilated_layers": 9,
"num_strands": 2,
"num_tasks": 4,
"pad_token_id": 4,
"profile_kernel_size": 25,
"stem_kernel_size": 25,
"tie_word_embeddings": true,
"transformers_version": "5.7.0",
"unk_token_id": 3,
"vocab_size": 5
}
|