Text Classification
setfit
Safetensors
sentence-transformers
bert
absa
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use omymble/books-full-bge-aspect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use omymble/books-full-bge-aspect with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("omymble/books-full-bge-aspect") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use omymble/books-full-bge-aspect with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("omymble/books-full-bge-aspect") 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 config_setfit.json from omymble/books-full-bge-aspect: direct link, hf CLI and curl.
- Browser
- Download file 140 Bytes
-
https://huggingface.co/omymble/books-full-bge-aspect/resolve/main/config_setfit.json
- Command line
-
hf download hf://omymble/books-full-bge-aspect/config_setfit.json
-
curl -L -o config_setfit.json https://huggingface.co/omymble/books-full-bge-aspect/resolve/main/config_setfit.json
140 Bytes
| { | |
| "normalize_embeddings": false, | |
| "spacy_model": "en_core_web_lg", | |
| "span_context": 0, | |
| "labels": [ | |
| "no aspect", | |
| "aspect" | |
| ] | |
| } |