Instructions to use taskload/political-setfit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use taskload/political-setfit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("taskload/political-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] - setfit
How to use taskload/political-setfit with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("taskload/political-setfit") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from taskload/political-setfit: direct link, hf CLI and curl.
- Browser
- Download file 394 Bytes
-
https://huggingface.co/taskload/political-setfit/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://taskload/political-setfit/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/taskload/political-setfit/resolve/main/tokenizer_config.json
394 Bytes
| { | |
| "add_prefix_space": false, | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "errors": "replace", | |
| "mask_token": "<mask>", | |
| "name_or_path": "/root/.cache/torch/sentence_transformers/roberta-base", | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "special_tokens_map_file": null, | |
| "tokenizer_class": "RobertaTokenizer", | |
| "trim_offsets": true, | |
| "unk_token": "<unk>" | |
| } | |