使用方法
from sentence_transformers import SentenceTransformer
sentences = ["sentence1", "sentence2"]
model = SentenceTransformer('IYun-large-zh')
embeddings_1 = model.encode(sentences, normalize_embeddings=True)
embeddings_2 = model.encode(sentences, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
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Evaluation results
- cos_sim_pearson on MTEB AFQMCvalidation set self-reported57.377
- cos_sim_spearman on MTEB AFQMCvalidation set self-reported60.891
- euclidean_pearson on MTEB AFQMCvalidation set self-reported60.057
- euclidean_spearman on MTEB AFQMCvalidation set self-reported60.891
- manhattan_pearson on MTEB AFQMCvalidation set self-reported60.039
- manhattan_spearman on MTEB AFQMCvalidation set self-reported60.860
- cos_sim_pearson on MTEB ATECtest set self-reported57.297
- cos_sim_spearman on MTEB ATECtest set self-reported58.816
- euclidean_pearson on MTEB ATECtest set self-reported63.693
- euclidean_spearman on MTEB ATECtest set self-reported58.816