Instructions to use multimolecule/framepool with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/framepool with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/framepool") model = AutoModel.from_pretrained("multimolecule/framepool") inputs = tokenizer("UAGCUUAUCAGACUGAUGUUGA", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_state - Notebooks
- Google Colab
- Kaggle
File size: 1,075 Bytes
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"architectures": [
"FramepoolForSequencePrediction"
],
"bos_token_id": 1,
"conv_channels": 128,
"dense_dropout": 0.2,
"dense_sizes": [
64
],
"dilations": [
1,
1,
1
],
"dtype": "float32",
"eos_token_id": 2,
"head": {
"act": null,
"bias": true,
"dropout": 0.0,
"hidden_size": null,
"layer_norm_eps": 1e-12,
"loss_weight": null,
"num_labels": 1,
"output_name": null,
"problem_type": "regression",
"transform": null,
"transform_act": "gelu",
"type": null
},
"hidden_act": "relu",
"id2label": null,
"kernel_size": [
7,
7,
7
],
"label2id": null,
"library_index": 1,
"library_size": 2,
"mask_token_id": 4,
"model_type": "framepool",
"null_channel_id": 4,
"null_token_id": 5,
"num_conv_layers": 3,
"num_dense_layers": 1,
"num_labels": 1,
"only_max_pool": false,
"pad_token_id": 0,
"padding": "same",
"skip_connections": "residual",
"tie_word_embeddings": true,
"transformers_version": "5.9.0",
"unk_token_id": 3,
"vocab_size": 5
}
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