Feature Extraction
Model2Vec
Safetensors
sentence-transformers
English
embeddings
static-embeddings
sentence-similarity
Instructions to use minishlab/blink-8M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use minishlab/blink-8M with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("minishlab/blink-8M") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - sentence-transformers
How to use minishlab/blink-8M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("minishlab/blink-8M") 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
MTEB table: bekko rerun in float32
Browse files
README.md
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@@ -76,7 +76,7 @@ MTEB(eng, v2), all 41 tasks (mteb 2.27.1). Every model is evaluated at 256 dimen
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| **blink-8M** | **55.35** | **38.86** | **43.12** | **78.42** | 67.88 | 41.49 | 73.56 | 22.48 |
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| lattice-retrieval | 55.24 | 38.01 | 42.96 | 77.51 | 68.25 | **42.11** | 73.41 | 22.56 |
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| bekko-static-embedding-v1 |
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| potion-base-32M | 54.02 | 31.94 | 42.52 | 77.33 | **70.48** | 41.49 | 73.17 | 24.14 |
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| potion-retrieval-32M | 53.70 | 34.64 | 41.95 | 75.94 | 66.67 | 41.76 | 72.89 | 19.95 |
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| potion-base-8M | 53.30 | 31.22 | 41.79 | 76.62 | 70.41 | 39.88 | 72.45 | **25.14** |
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#### Summary
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- **Mean:** blink-8M has the highest mean (55.35), just ahead of lattice (55.24) and
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- **Retrieval-type tasks:** blink-8M is best on retrieval, reranking and pair classification. On retrieval it scores 38.86, against 38.01 for lattice and 31.22 for potion-base-8M. Note that this task has significant overlap with BEIR, see below.
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- **Other tasks:** the potion-base models are best on classification (70.48 and 70.41) and potion-base-8M on summarization (25.14), with blink-8M at 67.88 and 22.48. Bekko is best on STS and lattice on clustering.
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- **Against potion-base-8M:** at roughly the same size, blink-8M is 2.05 points higher on the mean and 7.64 points higher on retrieval, but 2.53 points lower on classification.
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|---|---:|---:|---:|---:|---:|---:|---:|---:|
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| **blink-8M** | **55.35** | **38.86** | **43.12** | **78.42** | 67.88 | 41.49 | 73.56 | 22.48 |
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| lattice-retrieval | 55.24 | 38.01 | 42.96 | 77.51 | 68.25 | **42.11** | 73.41 | 22.56 |
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| bekko-static-embedding-v1 | 55.21 | 37.62 | 42.54 | 78.32 | 68.42 | 40.77 | **74.60** | 22.24 |
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| potion-base-32M | 54.02 | 31.94 | 42.52 | 77.33 | **70.48** | 41.49 | 73.17 | 24.14 |
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| potion-retrieval-32M | 53.70 | 34.64 | 41.95 | 75.94 | 66.67 | 41.76 | 72.89 | 19.95 |
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| potion-base-8M | 53.30 | 31.22 | 41.79 | 76.62 | 70.41 | 39.88 | 72.45 | **25.14** |
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#### Summary
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- **Mean:** blink-8M has the highest mean (55.35), just ahead of lattice (55.24) and bekko (55.21).
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- **Retrieval-type tasks:** blink-8M is best on retrieval, reranking and pair classification. On retrieval it scores 38.86, against 38.01 for lattice and 31.22 for potion-base-8M. Note that this task has significant overlap with BEIR, see below.
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- **Other tasks:** the potion-base models are best on classification (70.48 and 70.41) and potion-base-8M on summarization (25.14), with blink-8M at 67.88 and 22.48. Bekko is best on STS and lattice on clustering.
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- **Against potion-base-8M:** at roughly the same size, blink-8M is 2.05 points higher on the mean and 7.64 points higher on retrieval, but 2.53 points lower on classification.
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