Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:295188
loss:WeightedMultiPositiveCachedLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Ehsanl/cncpt_e5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Ehsanl/cncpt_e5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Ehsanl/cncpt_e5") 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: 1,976 Bytes
3921d13 | 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 42 43 44 | {
"summary": {
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"dev-summary_cosine_accuracy@5": 0.798,
"dev-summary_cosine_accuracy@10": 0.877,
"dev-summary_cosine_precision@1": 0.458,
"dev-summary_cosine_precision@10": 0.15610000000000002,
"dev-summary_cosine_precision@25": 0.08628000000000001,
"dev-summary_cosine_recall@1": 0.05152065295815296,
"dev-summary_cosine_recall@10": 0.173055395992896,
"dev-summary_cosine_recall@25": 0.23789036102786104,
"dev-summary_cosine_ndcg@10": 0.23113588599657442,
"dev-summary_cosine_mrr@10": 0.5988781746031744,
"dev-summary_cosine_map@100": 0.12377712614452646
},
"question": {
"dev-question_cosine_accuracy@1": 0.312,
"dev-question_cosine_accuracy@5": 0.584,
"dev-question_cosine_accuracy@10": 0.669,
"dev-question_cosine_precision@1": 0.312,
"dev-question_cosine_precision@10": 0.09659999999999999,
"dev-question_cosine_precision@25": 0.051879999999999996,
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"dev-question_cosine_recall@10": 0.1758062826062826,
"dev-question_cosine_recall@25": 0.23278918026418027,
"dev-question_cosine_ndcg@10": 0.19077156506634219,
"dev-question_cosine_mrr@10": 0.4223615079365074,
"dev-question_cosine_map@100": 0.11703775434440343
},
"article_question": {
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"dev-article_question_cosine_accuracy@10": 0.608,
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"dev-article_question_cosine_precision@10": 0.0834,
"dev-article_question_cosine_precision@25": 0.04748,
"dev-article_question_cosine_recall@1": 0.05320595238095238,
"dev-article_question_cosine_recall@10": 0.1930401154401154,
"dev-article_question_cosine_recall@25": 0.27011396103896107,
"dev-article_question_cosine_ndcg@10": 0.18361063568884123,
"dev-article_question_cosine_mrr@10": 0.34262301587301586,
"dev-article_question_cosine_map@100": 0.1191213852280308
}
} |