Sentence Similarity
sentence-transformers
Safetensors
English
roberta
feature-extraction
Generated from Trainer
dataset_size:3000
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use trbeers/distilroberta-base-nli-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use trbeers/distilroberta-base-nli-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("trbeers/distilroberta-base-nli-v2") sentences = [ "An Indian woman is washing and cleaning dirty laundry at a lake and in the background is a kid who appears to have jumped into the lake.", "An Indian woman is doing her laundry in a lake.", "An Indian woman is putting her laundry into the machine.", "A girl is playing with a Slinky." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e070ffac6186287522f4eab2959436e7d482e49aa164a9af4dab0ae14a852484
- Size of remote file:
- 328 MB
- SHA256:
- 2d04664076a05862c67966c6d1b5a65a62002316b266dd1c610cae5c605d4532
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