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am-azadi
/
KaLM-embedding-multilingual-mini-v1_Fine_Tuned_1e

Sentence Similarity
sentence-transformers
Safetensors
qwen2
feature-extraction
Generated from Trainer
dataset_size:21769
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use am-azadi/KaLM-embedding-multilingual-mini-v1_Fine_Tuned_1e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use am-azadi/KaLM-embedding-multilingual-mini-v1_Fine_Tuned_1e with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("am-azadi/KaLM-embedding-multilingual-mini-v1_Fine_Tuned_1e")
    
    sentences = [
        "Blooming Canals of Venice, Italy.   by:  [IG] ",
        "This comparison shows the values of gasoline in Cádiz and in Gibraltar in 2021 The comparison of fuel prices circulates in Spain at least since 2018",
        "Genuine image of a Venice canal laden with lotus blossoms The lotus blossoms were digitally inserted into this image by a graphic artist",
        "PT deputy presented PL for police to carry unloaded weapons Bill \"5439/2022\" for police officers to carry unloaded weapons does not exist"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
KaLM-embedding-multilingual-mini-v1_Fine_Tuned_1e
1.99 GB
Ctrl+K
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  • 1 contributor
History: 3 commits
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  • .gitattributes
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  • README.md
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  • added_tokens.json
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  • config.json
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  • config_sentence_transformers.json
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  • merges.txt
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  • model.safetensors
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  • modules.json
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  • sentence_bert_config.json
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  • special_tokens_map.json
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  • tokenizer.json
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  • tokenizer_config.json
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  • vocab.json
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