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Transformers
Retrieval
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Eval Results
text-embeddings-inference
Instructions to use HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v2", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Add evaluation results for model HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v2 revision d2a21c232dc712ae8230af56d1027cf21b7864bf
#4
by Samoed - opened
- .eval_results/ArguAna.yaml +20 -0
.eval_results/ArguAna.yaml
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- dataset:
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id: mteb/arguana
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task_id: ArguAna_default_test
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revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
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value: 57.419
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notes: Obtained using MTEB v1.14.5
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source:
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url: https://github.com/embeddings-benchmark/mteb/
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name: Obtained using MTEB v1.14.5
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user: mteb
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- dataset:
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id: mteb/arguana
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task_id: ArguAna
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revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
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value: 57.419
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notes: Obtained using MTEB v1.14.5
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source:
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url: https://github.com/embeddings-benchmark/mteb/
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name: Obtained using MTEB v1.14.5
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user: mteb
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