Sentence Similarity
sentence-transformers
Safetensors
English
bert
biencoder
text-classification
sentence-pair-classification
semantic-similarity
semantic-search
retrieval
reranking
Generated from Trainer
dataset_size:9233417
loss:ArcFaceInBatchLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use redis/langcache-embed-experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use redis/langcache-embed-experimental with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("redis/langcache-embed-experimental") sentences = [ "Hayley Vaughan portrayed Ripa on the ABC daytime soap opera , `` All My Children `` , between 1990 and 2002 .", "Traxxpad is a music application for Sony 's PlayStation Portable published by Definitive Studios and developed by Eidos Interactive .", "Between 1990 and 2002 , Hayley Vaughan Ripa portrayed in the ABC soap opera `` All My Children `` .", "Between 1990 and 2002 , Ripa Hayley portrayed Vaughan in the ABC soap opera `` All My Children `` ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, step 500
Browse files
eval/Information-Retrieval_evaluation_test_predictions_cosine.jsonl
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eval/Information-Retrieval_evaluation_test_results.csv
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initial_metrics.json
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"test_cosine_mrr@1": 0.5861241448475948,
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"test_cosine_map@100": 0.7216697804426214,
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"test_cosine_auc_precision_cache_hit_ratio": -0.3575434494855893,
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"test_cosine_map@100": 0.7216697804426214,
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model.safetensors
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training_args.bin
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