Model Details
Lim is a general text embedding model(chinese),We are continuously optimizing it.
History
ใ2023-12-22ใPublished lim_base_zh_v0 model
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
model_name="liujiarik/lim_base_zh"
from sentence_transformers import SentenceTransformer
sentences = ['ๆๆขๆๆบๅทไบ', 'ๅฆๆๆๆขๆๆบๆไนๅ?']
model = SentenceTransformer(model_name)
embeddings = model.encode(sentences)
print(embeddings)
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Evaluation results
- accuracy on MTEB AmazonReviewsClassification (zh)test set self-reported46.666
- f1 on MTEB AmazonReviewsClassification (zh)test set self-reported43.881
- v_measure on MTEB CLSClusteringP2Ptest set self-reported33.555
- v_measure on MTEB CLSClusteringS2Stest set self-reported36.180
- map on MTEB CMedQAv1test set self-reported83.847
- mrr on MTEB CMedQAv1test set self-reported86.346
- map on MTEB CMedQAv2test set self-reported84.746
- mrr on MTEB CMedQAv2test set self-reported87.416
- cos_sim_accuracy on MTEB Cmnlivalidation set self-reported70.992
- cos_sim_ap on MTEB Cmnlivalidation set self-reported79.584
- cos_sim_f1 on MTEB Cmnlivalidation set self-reported73.012
- cos_sim_precision on MTEB Cmnlivalidation set self-reported67.091
- cos_sim_recall on MTEB Cmnlivalidation set self-reported80.079
- dot_accuracy on MTEB Cmnlivalidation set self-reported70.992
- dot_ap on MTEB Cmnlivalidation set self-reported79.587
- dot_f1 on MTEB Cmnlivalidation set self-reported73.012
- dot_precision on MTEB Cmnlivalidation set self-reported67.091
- dot_recall on MTEB Cmnlivalidation set self-reported80.079
- euclidean_accuracy on MTEB Cmnlivalidation set self-reported70.992
- euclidean_ap on MTEB Cmnlivalidation set self-reported79.584