Add new ColBERT model
Browse files- 2_Dense/config.json +7 -0
- 2_Dense/model.safetensors +3 -0
- README.md +3 -2
- modules.json +6 -0
2_Dense/config.json
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{
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"in_features": 128,
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"out_features": 768,
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"bias": false,
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"activation_function": "torch.nn.modules.linear.Identity",
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"use_residual": false
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}
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2_Dense/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:1b9aa25fbcb8a9175185ee625fa7d0460cf409797116efe226b55473c8e034ba
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size 196696
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README.md
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@@ -24,7 +24,7 @@ library_name: PyLate
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# Redis fine-tuned late-interaction ColBERT model for semantic caching on LangCache
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This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [lightonai/GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) on the [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) dataset. It maps sentences & paragraphs to sequences of
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## Model Details
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- **Base model:** [lightonai/GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) <!-- at revision 6605e431bed9b582d3eff7699911d2b64e8ccd3f -->
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- **Document Length:** 512 tokens
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- **Query Length:** 512 tokens
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- **Output Dimensionality:**
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- **Similarity Function:** MaxSim
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- **Training Dataset:**
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- [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1)
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ColBERT(
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(0): Transformer({'max_seq_length': 511, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
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(1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
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)
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```
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# Redis fine-tuned late-interaction ColBERT model for semantic caching on LangCache
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This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [lightonai/GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) on the [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) dataset. It maps sentences & paragraphs to sequences of 768-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
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## Model Details
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- **Base model:** [lightonai/GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) <!-- at revision 6605e431bed9b582d3eff7699911d2b64e8ccd3f -->
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- **Document Length:** 512 tokens
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- **Query Length:** 512 tokens
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- **Output Dimensionality:** 768 tokens
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- **Similarity Function:** MaxSim
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- **Training Dataset:**
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- [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1)
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ColBERT(
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(0): Transformer({'max_seq_length': 511, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
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(1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
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(2): Dense({'in_features': 128, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
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)
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```
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modules.json
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"name": "1",
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"path": "1_Dense",
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"type": "pylate.models.Dense.Dense"
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}
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]
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"name": "1",
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"path": "1_Dense",
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"type": "pylate.models.Dense.Dense"
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},
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{
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"idx": 2,
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"name": "2",
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"path": "2_Dense",
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"type": "pylate.models.Dense.Dense"
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}
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]
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