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
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- 2_Dense/model.safetensors +3 -0
- model.safetensors +3 -0
2_Dense/model.safetensors
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model.safetensors
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oid sha256:2c995c98129829b8dfc07e999da8a376712e6cd7257b702ac45fed8a68f0f227
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size 1883734344
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