Text Classification
PyTorch
Safetensors
sentence-transformers
Spanish
multitask-learning
spanish
intent-classification
Instructions to use belta1/bc_pibot_intent_setfit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use belta1/bc_pibot_intent_setfit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("belta1/bc_pibot_intent_setfit") 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
Download encoder/tokenizer_config.json from belta1/bc_pibot_intent_setfit: direct link, hf CLI and curl.
- Browser
- Download file 390 Bytes
-
https://huggingface.co/belta1/bc_pibot_intent_setfit/resolve/main/encoder/tokenizer_config.json
- Command line
-
hf download hf://belta1/bc_pibot_intent_setfit/encoder/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/belta1/bc_pibot_intent_setfit/resolve/main/encoder/tokenizer_config.json
390 Bytes
| { | |
| "backend": "tokenizers", | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "is_local": false, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 256, | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |