Instructions to use liujiarik/lim_base_zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liujiarik/lim_base_zh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="liujiarik/lim_base_zh")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liujiarik/lim_base_zh") model = AutoModel.from_pretrained("liujiarik/lim_base_zh") - Notebooks
- Google Colab
- Kaggle
Model Details
Lim is a general text embedding model(chinese),We are continuously optimizing it.
History
γ2023-12-22γPublished 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 = ['ζζ’ζζΊε·δΊ', 'ε¦ζζζ’ζζΊζδΉε?']
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