Feature Extraction
Transformers
PyTorch
English
fill-mask
genomics
virology
dnabert
foundation-model
hvilm
pathogenicity
transmissibility
host-tropism
viral-genomics
custom_code
Instructions to use duttaprat/HViLM-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use duttaprat/HViLM-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="duttaprat/HViLM-base", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("duttaprat/HViLM-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "trust_remote_code": true, | |
| "_name_or_path": "zhihan1996/DNABERT-2-117M", | |
| "alibi_starting_size": 512, | |
| "architectures": [ | |
| "BertForMaskedLM" | |
| ], | |
| "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", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.28.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 4096 | |
| } | |