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:
- 2e122f6a4536963d2758e51f8ebda26d6036463498e50bbba7aed0cd9b4b60e6
- Size of remote file:
- 5.56 kB
- SHA256:
- c86c8b547af514d0800de58a4a7d5b9ffc1d3394e4425109829467a718441389
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.