Sentence Similarity
Safetensors
sentence-transformers
Italian
pylate
modernbert
colbert
late-interaction
italian
retrieval
information-retrieval
rag
multi-vector
text-embeddings-inference
Instructions to use enricollen/ItColBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use enricollen/ItColBERT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("enricollen/ItColBERT") sentences = [ "Questa è una persona felice", "Questo è un cane felice", "Questa è una persona molto felice", "Oggi è una giornata di sole" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Add new ColBERT model
Browse files
README.md
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@@ -311,10 +311,10 @@ comparison, by a wide margin.
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| bge-m3 (dense) | 568M | 0.4531 |
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| jina-colbert-v2 | ~0.6B | 0.3858 † |
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At roughly a quarter to a sixth the size of the ~560M-parameter multilingual
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giants, ItColBERT beats `SauerkrautLM-Multi-ModernColBERT` (the same size
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class) and `ColBERT-XM` (2× the parameters) outright, and statistically ties
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`jina-colbert-v2` (~4.4× the parameters) on the primary out-of-domain
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benchmark. `mLateOn` is the one model that beats it outright while also being
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smaller than the dense giants — included here rather than left out, since
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citing only the flattering comparisons would defeat the point of this
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| 311 |
| bge-m3 (dense) | 568M | 0.4531 |
|
| 312 |
| jina-colbert-v2 | ~0.6B | 0.3858 † |
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| 313 |
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+
At roughly a quarter to a sixth the size of the \~560M-parameter multilingual
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| 315 |
giants, ItColBERT beats `SauerkrautLM-Multi-ModernColBERT` (the same size
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| 316 |
class) and `ColBERT-XM` (2× the parameters) outright, and statistically ties
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| 317 |
+
`jina-colbert-v2` (\~4.4× the parameters) on the primary out-of-domain
|
| 318 |
benchmark. `mLateOn` is the one model that beats it outright while also being
|
| 319 |
smaller than the dense giants — included here rather than left out, since
|
| 320 |
citing only the flattering comparisons would defeat the point of this
|