Instructions to use HasinMDG/Topic_mpnet_L2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HasinMDG/Topic_mpnet_L2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HasinMDG/Topic_mpnet_L2") 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/Topic_mpnet_L2 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("HasinMDG/Topic_mpnet_L2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 391414528f7c1a60fe0ebebea18a5320c7248104bd622d39573e7c6b90d318e4
- Size of remote file:
- 395 kB
- SHA256:
- 80bdfbe76c4cc59976ab145aaacbd3d5b61dba26b438c2ace2c70376119dc6db
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