Sentence Similarity
Safetensors
sentence-transformers
German
PyLate
bert
ColBERT
text-embeddings-inference
Instructions to use samheym/GerColBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use samheym/GerColBERT 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="samheym/GerColBERT") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
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README.md
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- Training Dataset: samheym/ger-dpr-collection
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- Dataset: 10% of randomly selected triples from the final dataset
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- Vector Length: 128
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- Maximum Document Length: 256
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- Batch Size: 50
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- Training Steps: 80,000
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- Gradient Accumulation: 1 step
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- Training Dataset: samheym/ger-dpr-collection
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- Dataset: 10% of randomly selected triples from the final dataset
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- Vector Length: 128
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- Maximum Document Length: 256 Tokens
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- Batch Size: 50
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- Training Steps: 80,000
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- Gradient Accumulation: 1 step
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