Text Classification
setfit
Safetensors
sentence-transformers
deberta-v2
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use NaveenKumar96/intent-setfit-deberta-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use NaveenKumar96/intent-setfit-deberta-v1 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("NaveenKumar96/intent-setfit-deberta-v1") - sentence-transformers
How to use NaveenKumar96/intent-setfit-deberta-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NaveenKumar96/intent-setfit-deberta-v1") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_prefix_space": true, | |
| "backend": "tokenizers", | |
| "bos_token": "[CLS]", | |
| "cls_token": "[CLS]", | |
| "do_lower_case": false, | |
| "eos_token": "[SEP]", | |
| "extra_special_tokens": [ | |
| "[PAD]", | |
| "[CLS]", | |
| "[SEP]" | |
| ], | |
| "is_local": true, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "split_by_punct": false, | |
| "tokenizer_class": "DebertaV2Tokenizer", | |
| "unk_id": 3, | |
| "unk_token": "[UNK]", | |
| "vocab_type": "spm" | |
| } | |