Feature Extraction
Transformers
ONNX
English
bert
sparse
sparsity
quantized
embeddings
int8
mteb
deepsparse
Eval Results (legacy)
Instructions to use RedHatAI/bge-large-en-v1.5-quant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/bge-large-en-v1.5-quant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RedHatAI/bge-large-en-v1.5-quant")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RedHatAI/bge-large-en-v1.5-quant") model = AutoModel.from_pretrained("RedHatAI/bge-large-en-v1.5-quant") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5965a407375759b2cfa5ffbcc2504d236752cc7011c8e0921ee3718c17f1a449
- Size of remote file:
- 431 MB
- SHA256:
- 19910b154c3b60a70467d4cf01df40d0d5136dc06859396722c4d06f008a4178
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