Sentence Similarity
ONNX
Safetensors
sentence-transformers
Transformers
English
PyLate
modernbert
feature-extraction
ColBERT
multi-vector
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use mixedbread-ai/mxbai-edge-colbert-v0-17m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mixedbread-ai/mxbai-edge-colbert-v0-17m 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="mixedbread-ai/mxbai-edge-colbert-v0-17m") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Transformers
How to use mixedbread-ai/mxbai-edge-colbert-v0-17m with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mixedbread-ai/mxbai-edge-colbert-v0-17m") model = AutoModel.from_pretrained("mixedbread-ai/mxbai-edge-colbert-v0-17m", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update 1_Dense/config.json
Browse files- 1_Dense/config.json +1 -1
1_Dense/config.json
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"in_features": 256,
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"out_features": 512,
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"bias": false,
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"activation_function": "torch.nn.modules.linear.Identity"
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}
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"in_features": 256,
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"out_features": 512,
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"bias": false,
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"activation_function": "torch.nn.modules.linear.Identity"
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}
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