Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Rust
ONNX
Safetensors
OpenVINO
Transformers
English
bert
feature-extraction
Eval Results
text-embeddings-inference
Instructions to use sentence-transformers/all-MiniLM-L6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/all-MiniLM-L6-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use sentence-transformers/all-MiniLM-L6-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2") model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2") - Inference
- Notebooks
- Google Colab
- Kaggle
Add evaluation results for model sentence-transformers/all-MiniLM-L6-v2 revision 8b3219a92973c328a8e22fadcfa821b5dc75636a
#154
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: 50.167
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notes: Obtained using MTEB v1.12.75
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source:
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url: https://github.com/embeddings-benchmark/mteb/
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name: Obtained using MTEB v1.12.75
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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: 50.167
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notes: Obtained using MTEB v1.12.75
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source:
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url: https://github.com/embeddings-benchmark/mteb/
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name: Obtained using MTEB v1.12.75
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user: mteb
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