Instructions to use multimolecule/xpresso with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/xpresso with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/xpresso") model = AutoModel.from_pretrained("multimolecule/xpresso") - Notebooks
- Google Colab
- Kaggle
File size: 1,034 Bytes
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"architectures": [
"XpressoForSequencePrediction"
],
"bos_token_id": null,
"conv_channels": [
128,
32
],
"conv_dilations": [
1,
1
],
"conv_kernel_sizes": [
6,
9
],
"dtype": "float32",
"eos_token_id": null,
"fc_dims": [
64,
2
],
"head": {
"act": null,
"bias": true,
"dropout": 0.0,
"hidden_size": 2,
"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",
"hidden_dropout": 0.00099,
"hidden_size": 2,
"id2label": null,
"input_length": 10500,
"label2id": null,
"mask_token_id": null,
"model_type": "xpresso",
"null_token_id": null,
"num_conv_layers": 2,
"num_features": 6,
"num_labels": 1,
"pad_token_id": 4,
"pool_sizes": [
30,
10
],
"tie_word_embeddings": true,
"transformers_version": "5.7.0",
"unk_token_id": 4,
"vocab_size": 5
}
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