Text Classification
Transformers
Safetensors
English
edge-computing
service-orchestration
intent-classification
Instructions to use UTSCybeR/Edge-Computing-JEV-classifiers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UTSCybeR/Edge-Computing-JEV-classifiers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UTSCybeR/Edge-Computing-JEV-classifiers")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UTSCybeR/Edge-Computing-JEV-classifiers", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 351 Bytes
786cc60 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"backend": "tokenizers",
"cls_token": "[CLS]",
"do_lower_case": true,
"is_local": false,
"local_files_only": false,
"mask_token": "[MASK]",
"model_max_length": 512,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
|