Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
fitness
retrieval
text-embeddings-inference
Instructions to use OrDora/coachtwin-embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use OrDora/coachtwin-embedder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("OrDora/coachtwin-embedder") 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
File size: 1,299 Bytes
01e854c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | {
"embedding_model": "BAAI/bge-small-en-v1.5",
"dim": 384,
"n_vectors": 10393,
"normalized": true,
"metric": "inner_product (== cosine on normalised vectors)",
"doc_text_template": "{title}. Goal: {goal}. Equipment: {equipment}. Duration: {duration_minutes} minutes. Difficulty: {difficulty}. Body focus: {body_focus}. Exercises: {names}.",
"query_text_template": "I want a {difficulty} {goal} workout for {body_focus}, {duration} minutes, using {equipment}.",
"needs_query_prefix": true,
"evaluation": {
"all-MiniLM-L6-v2": {
"precision_at_3_strict": 0.4733,
"precision_at_3_loose": 0.6667,
"mrr_at_10": 0.6686,
"params_M": 22,
"dim": 384,
"encode_corpus_s": 7.0
},
"all-mpnet-base-v2": {
"precision_at_3_strict": 0.5047,
"precision_at_3_loose": 0.7453,
"mrr_at_10": 0.6866,
"params_M": 110,
"dim": 768,
"encode_corpus_s": 31.2
},
"bge-small-en-v1.5": {
"precision_at_3_strict": 0.6687,
"precision_at_3_loose": 0.8273,
"mrr_at_10": 0.802,
"params_M": 33,
"dim": 384,
"encode_corpus_s": 10.4
}
},
"random_baseline": {
"precision_at_3_strict": 0.021999999999999995,
"precision_at_3_loose": 0.12,
"mrr_at_10": 0.06852301587301587
}
} |