Instructions to use HasinMDG/SetFit_Labse_Sentiment_Towards_Topic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HasinMDG/SetFit_Labse_Sentiment_Towards_Topic with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HasinMDG/SetFit_Labse_Sentiment_Towards_Topic") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 2_Dense/pytorch_model.bin from HasinMDG/SetFit_Labse_Sentiment_Towards_Topic: direct link, hf CLI and curl.
- Browser
- Download file 2.36 MB
-
https://huggingface.co/HasinMDG/SetFit_Labse_Sentiment_Towards_Topic/resolve/refs%2Fpr%2F1/2_Dense/pytorch_model.bin
- Command line
-
hf download hf://HasinMDG/SetFit_Labse_Sentiment_Towards_Topic@refs/pr/1/2_Dense/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/HasinMDG/SetFit_Labse_Sentiment_Towards_Topic/resolve/refs%2Fpr%2F1/2_Dense/pytorch_model.bin
2.36 MB
- Xet hash:
- 24e2b2f82363e4137df43bd07900ffafe23d58df886167645eff891514f0b69e
- Size of remote file:
- 2.36 MB
- SHA256:
- fc61806c62e9f64f0f2a4b243c61db60c440da3ed8945072cb5a82f55ab0fab7
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