Token Classification
Transformers
Safetensors
esm
text-classification
virulence-prediction
biology
bioinformatics
microbiology
Instructions to use kssrikar4/AVP-ESM2-8m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kssrikar4/AVP-ESM2-8m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kssrikar4/AVP-ESM2-8m")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kssrikar4/AVP-ESM2-8m") model = AutoModelForSequenceClassification.from_pretrained("kssrikar4/AVP-ESM2-8m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 780 Bytes
7414e4d | 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 | {
"architectures": [
"EsmForSequenceClassification"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"emb_layer_norm_before": false,
"esmfold_config": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 320,
"initializer_range": 0.02,
"intermediate_size": 1280,
"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": 6,
"pad_token_id": 1,
"position_embedding_type": "rotary",
"problem_type": "single_label_classification",
"token_dropout": true,
"torch_dtype": "float32",
"transformers_version": "4.55.2",
"use_cache": true,
"vocab_list": null,
"vocab_size": 33
}
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