Instructions to use zeroshot/gte-base-sparse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zeroshot/gte-base-sparse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="zeroshot/gte-base-sparse")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("zeroshot/gte-base-sparse") model = AutoModel.from_pretrained("zeroshot/gte-base-sparse") - Notebooks
- Google Colab
- Kaggle
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This is the sparse ONNX variant of the [gte-base](https://huggingface.co/thenlper/gte-base) embeddings model created with [DeepSparse Optimum](https://github.com/neuralmagic/optimum-deepsparse) for ONNX export/inference and Neural Magic's [Sparsify](https://github.com/neuralmagic/sparsify) for one-shot quantization (INT8) and unstructured pruning 50%.
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Current list of sparse and quantized gte
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This is the sparse ONNX variant of the [gte-base](https://huggingface.co/thenlper/gte-base) embeddings model created with [DeepSparse Optimum](https://github.com/neuralmagic/optimum-deepsparse) for ONNX export/inference and Neural Magic's [Sparsify](https://github.com/neuralmagic/sparsify) for one-shot quantization (INT8) and unstructured pruning 50%.
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Current list of sparse and quantized gte ONNX models:
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| Links | Sparsification Method |
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