Feature Extraction
Transformers
PyTorch
ONNX
Safetensors
English
bert
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use BAAI/bge-base-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BAAI/bge-base-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/bge-base-en")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-base-en") model = AutoModel.from_pretrained("BAAI/bge-base-en", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from BAAI/bge-base-en: direct link, hf CLI and curl.
- Browser
- Download file 366 Bytes
-
https://huggingface.co/BAAI/bge-base-en/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://BAAI/bge-base-en/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/BAAI/bge-base-en/resolve/main/tokenizer_config.json
366 Bytes
| { | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |