Instructions to use Pristinenlp/alime-embedding-large-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pristinenlp/alime-embedding-large-zh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Pristinenlp/alime-embedding-large-zh")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Pristinenlp/alime-embedding-large-zh") model = AutoModel.from_pretrained("Pristinenlp/alime-embedding-large-zh") - Notebooks
- Google Colab
- Kaggle
alime-embedding-large-zh
The alime embedding 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:
from sentence_transformers import SentenceTransformer
sentences = ["西湖在哪?", "西湖风景名胜区位于浙江省杭州市"]
model = SentenceTransformer('Pristinenlp/alime-embedding-large-zh')
embeddings = model.encode(sentences, normalize_embeddings=True)
print(embeddings)
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Spaces using Pristinenlp/alime-embedding-large-zh 11
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mteb/leaderboard_legacy
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sq66/leaderboard_legacy
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reader-1/1
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shiwan7788/leaderboard-uni
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SmileXing/leaderboard
Evaluation results
- cos_sim_pearson on MTEB AFQMCvalidation set self-reported49.648
- cos_sim_spearman on MTEB AFQMCvalidation set self-reported54.733
- euclidean_pearson on MTEB AFQMCvalidation set self-reported53.063
- euclidean_spearman on MTEB AFQMCvalidation set self-reported54.733
- manhattan_pearson on MTEB AFQMCvalidation set self-reported53.048
- manhattan_spearman on MTEB AFQMCvalidation set self-reported54.729
- cos_sim_pearson on MTEB ATECtest set self-reported48.659
- cos_sim_spearman on MTEB ATECtest set self-reported55.125