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
File size: 370 Bytes
3417dfc | 1 2 3 4 5 6 7 8 9 10 11 | {
"base_model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
"n_train_pairs": 225516,
"n_test_pairs": 1000,
"retrieval_top1_before": 0.017,
"retrieval_top1_after": 0.928,
"retrieval_top5_before": 0.045,
"retrieval_top5_after": 0.982,
"mean_cosine_aligned_before": 0.15399053692817688,
"mean_cosine_aligned_after": 0.7013674378395081
} |