Text Classification
Transformers
PyTorch
genomics
virology
dna
virus
pathogenicity
hvue-v2
custom_code
Instructions to use duttaprat/HViLM-Patho with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use duttaprat/HViLM-Patho with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="duttaprat/HViLM-Patho", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("duttaprat/HViLM-Patho", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,172 Bytes
c66d275 | 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 | {
"_name_or_path": "/data/projects/Virus/revision/hvue_v2/models/HViLM-base",
"alibi_starting_size": 512,
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.0,
"auto_map": {
"AutoConfig": "configuration_bert.BertConfig",
"AutoModel": "bert_layers.BertModel",
"AutoModelForMaskedLM": "bert_layers.BertForMaskedLM",
"AutoModelForSequenceClassification": "bert_layers.BertForSequenceClassification"
},
"classifier_dropout": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"num_attention_heads": 12,
"num_hidden_layers": 12,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.28.0",
"trust_remote_code": true,
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 4096,
"id2label": {
"0": "NON_PATHOGENIC",
"1": "PATHOGENIC"
},
"label2id": {
"NON_PATHOGENIC": 0,
"PATHOGENIC": 1
}
} |