Feature Extraction
Transformers
PyTorch
Safetensors
English
bert
mteb
sentence-transfomres
Eval Results (legacy)
text-embeddings-inference
Instructions to use BAAI/bge-large-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BAAI/bge-large-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/bge-large-en")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-large-en") model = AutoModel.from_pretrained("BAAI/bge-large-en", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46f25006649298a34ce3a90bdedc9822231fc906c8786c4a7ae5d798dd2d64c8
- Size of remote file:
- 1.34 GB
- SHA256:
- 5ccd0ac7807e3ebe6a00c9bf11ac6cdd9a475bab954e8a86b90adcb4372c624d
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