Text Classification
setfit
PyTorch
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use gsjang/sroberta-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use gsjang/sroberta-embedding with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("gsjang/sroberta-embedding") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use gsjang/sroberta-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gsjang/sroberta-embedding") 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 tokenizer_config.json from gsjang/sroberta-embedding: direct link, hf CLI and curl.
- Browser
- Download file 606 Bytes
-
https://huggingface.co/gsjang/sroberta-embedding/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://gsjang/sroberta-embedding/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/gsjang/sroberta-embedding/resolve/main/tokenizer_config.json
606 Bytes
| { | |
| "bos_token": "[CLS]", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": false, | |
| "eos_token": "[SEP]", | |
| "mask_token": "[MASK]", | |
| "max_length": 128, | |
| "model_max_length": 512, | |
| "never_split": null, | |
| "pad_to_multiple_of": null, | |
| "pad_token": "[PAD]", | |
| "pad_token_type_id": 0, | |
| "padding_side": "right", | |
| "sep_token": "[SEP]", | |
| "stride": 0, | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": "[UNK]" | |
| } | |