Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:3696
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use romain125/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use romain125/model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("romain125/model") 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:
- 7363fb20574662e56cdbaa3f987e62db054729da4c571a6a144c0b162d45c304
- Size of remote file:
- 5.56 kB
- SHA256:
- 9b7a8c5bc76ea051be4e6c8e0a46e14a4698cbbd36637d4adda759ded9c4a66d
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