Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:1533351
loss:MultipleNegativesRankingLoss
loss:SoftmaxLoss
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use trmteb/multilingual-e5-base_fine_tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use trmteb/multilingual-e5-base_fine_tuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("trmteb/multilingual-e5-base_fine_tuned") sentences = [ "CIA, filmi indirdi ve filmi ertesi gün Birleşmiş Milletlere götürdü.", "Bir açıklama yapmalısın! Wolverstone'a ne oldu?", "CIA, BM’nin filmi hemen görmesi gerektiğini düşünüyordu.", "Benim yolum en zor yoldur." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 900d575674d5bfcd5c2b9059c9221a42f92c957ed7230f7e096d3e9580c3e4c2
- Size of remote file:
- 17.1 MB
- SHA256:
- 883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
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