Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:5302
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use romain125/debug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use romain125/debug with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("romain125/debug") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 48ffb39d82f5aca775980d6f2ea5770e73e05f862d81376867de6cd2c9bcdd63
- Size of remote file:
- 1.11 GB
- SHA256:
- 1154e52c0e756662663701a53abbc356004d613a03a917d98dc8b06e2f6f8c1d
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