Text Classification
setfit
Safetensors
sentence-transformers
mpnet
absa
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-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 model.safetensors from NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect/resolve/main/model.safetensors
- Command line
-
hf download hf://NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect/resolve/main/model.safetensors
438 MB
- Xet hash:
- 934635f8dab61434377fbcb64c6d9d7b83355b4bb92f5811f98a8ce70a9b4cc7
- Size of remote file:
- 438 MB
- SHA256:
- e10c8516f4ddde135d3e4e7730a2963084485547e5a0f661a6a21fb1fda99cce
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