Text Classification
setfit
Safetensors
sentence-transformers
mpnet
absa
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-polarity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-polarity with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-polarity") - sentence-transformers
How to use joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-polarity with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-polarity") 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
File size: 172 Bytes
a4331d2 | 1 2 3 4 5 6 7 8 9 10 11 | {
"spacy_model": "en_core_web_sm",
"labels": [
"conflict",
"negative",
"neutral",
"positive"
],
"normalize_embeddings": false,
"span_context": 3
} |