Feature Extraction
Transformers
PyTorch
English
distilbert
dense-retrieval
knowledge-distillation
text-embeddings-inference
Instructions to use LilaBoualili/colbert-distilbert-margin_mse-T2-msmarco-encoder-only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LilaBoualili/colbert-distilbert-margin_mse-T2-msmarco-encoder-only with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LilaBoualili/colbert-distilbert-margin_mse-T2-msmarco-encoder-only")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LilaBoualili/colbert-distilbert-margin_mse-T2-msmarco-encoder-only") model = AutoModel.from_pretrained("LilaBoualili/colbert-distilbert-margin_mse-T2-msmarco-encoder-only") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b7a9c3d7b16ffaed0a3b2ccbbb841fa77fa951c22afd068b10456a140a2c11f
- Size of remote file:
- 265 MB
- SHA256:
- f9accca290163b7d9d6b5b837c1e0910d475b5f26c70fd7290ef36efff7e623b
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