Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:225516
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use 2ADT-Consulting/susu-sentence-encoder-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use 2ADT-Consulting/susu-sentence-encoder-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("2ADT-Consulting/susu-sentence-encoder-v1") sentences = [ "Il leur donna cet ordre: «Vous transmettrez ce message à mon seigneur Esaü: Voici ce que dit ton serviteur Jacob: J'ai séjourné chez Laban et j'y suis resté jusqu'à maintenant.", "I na fe nde to, i naxa gbata sigafe ra kiiti banxi, barima xa fe gbɛtɛ minima i mu naxan kolon, na nɔma i rayaagide i dɔxɔboore ya xɔri.", "Wo xa sɔɔrie luxi nɛ alɔ tugumi naxee na tɛtɛ fari gɛɛsɛgɛ, kɔnɔ soge na te, e tugan, e siga yire mixi mu dɛnnaxɛ kolon.", "A naxa a fala e bɛ, «Wo yi nan falama n marigi Esayu bɛ, ‹I xa konyi di Yaxuba naxɛ, N bara bu Laban xɔnyi han ya." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Add bilingual (fr/en-sus) sentence encoder fine-tuned with MNRL on paraphrase-multilingual-MiniLM-L12-v2
cdd2b74 verified - Xet hash:
- c9abfbcdb4ffc00d355a5aceeb3df3cad5ed9f079223b9434115e96b6423ffb1
- Size of remote file:
- 17.1 MB
- SHA256:
- cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
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