Instructions to use HasinMDG/Labse_Sentiment_Toward_Topics_Baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HasinMDG/Labse_Sentiment_Toward_Topics_Baseline with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HasinMDG/Labse_Sentiment_Toward_Topics_Baseline") 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] - setfit
How to use HasinMDG/Labse_Sentiment_Toward_Topics_Baseline with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("HasinMDG/Labse_Sentiment_Toward_Topics_Baseline") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 096725547ca9ee4c1a2fa75b7c00cae3785d748d97870553fa8e5b88be75f52b
- Size of remote file:
- 1.88 GB
- SHA256:
- f56472925e4b5986e1d7a64c5eb4fcb10b0e7192237ccbf21c88d1e4dd2d5b71
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