Sentence Similarity
Safetensors
sentence-transformers
PyLate
modernbert
ColBERT
feature-extraction
Generated from Trainer
dataset_size:640000
loss:Distillation
Eval Results (legacy)
text-embeddings-inference
Instructions to use patrick-358/my-gte-colbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use patrick-358/my-gte-colbert with sentence-transformers:
from pylate import models queries = [ "Which planet is known as the Red Planet?", "What is the largest planet in our solar system?", ] documents = [ ["Mars is the Red Planet.", "Venus is Earth's twin."], ["Jupiter is the largest planet.", "Saturn has rings."], ] model = models.ColBERT(model_name_or_path="patrick-358/my-gte-colbert") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
Piekarski, Patryk (ext) commited on
Commit ·
985e106
1
Parent(s): 78d50a1
Add Pooling module for TEI
Browse files- 1_Pooling/config.json +10 -0
- modules.json +3 -12
1_Pooling/config.json
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": false
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}
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modules.json
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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},
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{
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"idx": 1,
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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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{ "idx": 0, "name": "0", "path": "", "type": "sentence_transformers.models.Transformer" },
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{ "idx": 1, "name": "1", "path": "1_Pooling", "type": "sentence_transformers.models.Pooling" },
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{ "idx": 2, "name": "2", "path": "2_Dense", "type": "sentence_transformers.models.Dense" }
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