Instructions to use isikz/esm1b_multitask_mlm_cls_ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use isikz/esm1b_multitask_mlm_cls_ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="isikz/esm1b_multitask_mlm_cls_ft")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("isikz/esm1b_multitask_mlm_cls_ft") model = AutoModel.from_pretrained("isikz/esm1b_multitask_mlm_cls_ft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 766 Bytes
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"_name_or_path": "facebook/esm1b_t33_650M_UR50S",
"architectures": [
"EsmModel"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"emb_layer_norm_before": true,
"esmfold_config": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1280,
"initializer_range": 0.02,
"intermediate_size": 5120,
"is_folding_model": false,
"layer_norm_eps": 1e-05,
"mask_token_id": 32,
"max_position_embeddings": 1026,
"model_type": "esm",
"num_attention_heads": 20,
"num_hidden_layers": 33,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"token_dropout": true,
"torch_dtype": "float32",
"transformers_version": "4.45.2",
"use_cache": true,
"vocab_list": null,
"vocab_size": 33
}
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