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
Download tokenizer_config.json from duttaprat/HViLM-Patho: direct link, hf CLI and curl.
- Browser
- Download file 284 Bytes
-
https://huggingface.co/duttaprat/HViLM-Patho/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://duttaprat/HViLM-Patho/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/duttaprat/HViLM-Patho/resolve/main/tokenizer_config.json
284 Bytes
| { | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]", | |
| "model_max_length": 250, | |
| "pad_token": "[PAD]", | |
| "padding_side": "right", | |
| "sep_token": "[SEP]", | |
| "token": null, | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "unk_token": "[UNK]" | |
| } | |