Instructions to use NoesisLab/Collins-Embedding-3M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NoesisLab/Collins-Embedding-3M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NoesisLab/Collins-Embedding-3M") 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] - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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@@ -81,7 +81,7 @@ With V = 30522 and B = 512, Collins uses ~60× fewer parameters for the token en
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| GloVe (6B, 300d) | Static Embedding | ~120M | ~0.50 | 0.0042 |
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| BERT-base (Mean Pool) | Contextual (no NLI FT) | 110M | ~0.50 | 0.0045 |
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| **Collins
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| paraphrase-MiniLM-L3-v2 | Contextual | 15M | ~0.75 | 0.050 |
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| BGE-micro-v2 | Contextual | 17M | ~0.76 | 0.044 |
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| paraphrase-MiniLM-L6-v2 | Contextual | 22M | ~0.79 | 0.036 |
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| GloVe (6B, 300d) | Static Embedding | ~120M | ~0.50 | 0.0042 |
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| BERT-base (Mean Pool) | Contextual (no NLI FT) | 110M | ~0.50 | 0.0045 |
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| **Collins (Ours)** | **Hash + RoPE** | **3M** | **0.7114** | **0.237** |
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| paraphrase-MiniLM-L3-v2 | Contextual | 15M | ~0.75 | 0.050 |
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| BGE-micro-v2 | Contextual | 17M | ~0.76 | 0.044 |
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| 87 |
| paraphrase-MiniLM-L6-v2 | Contextual | 22M | ~0.79 | 0.036 |
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