Text Classification
setfit
Safetensors
sentence-transformers
bert
absa
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect") - sentence-transformers
How to use joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-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
- Xet hash:
- 4b059a1bc46c4cb9197bcd8236e8834bc39cb662a49c703914e834537d79c1e3
- Size of remote file:
- 3.92 kB
- SHA256:
- 95a94b6d35296d4ec451dca25a4970baca68060fc8a05eea35fa5d556f05ac85
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