Sentence Similarity
sentence-transformers
Safetensors
English
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:80
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use AKL126/result_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AKL126/result_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AKL126/result_model") sentences = [ "Two adults, one female in white, with shades and one male, gray clothes, walking across a street, away from a eatery with a blurred image of a dark colored red shirted person in the foreground.", "Two people ride bicycles into a tunnel.", "The friends scowl at each other over a full dinner table.", "A team is trying to score the games winning out." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9205f742985d361cbdd676caf2c62bfee798f003c343fe1837ff90062c52631
- Size of remote file:
- 5.52 kB
- SHA256:
- d6ea488ee6e9089a524722aa1c00b55b8ba2c4f57cb4cc27c18463a3d0bcca89
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