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
Fix `model_max_length` in `tokenizer_config.json`
#7
by bryant1410 - opened
- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
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@@ -4,7 +4,7 @@
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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-
"model_max_length":
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| 8 |
"never_split": null,
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| 9 |
"pad_token": "[PAD]",
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| 10 |
"sep_token": "[SEP]",
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| 4 |
"do_basic_tokenize": true,
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| 5 |
"do_lower_case": true,
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"mask_token": "[MASK]",
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| 7 |
+
"model_max_length": 512,
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| 8 |
"never_split": null,
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| 9 |
"pad_token": "[PAD]",
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| 10 |
"sep_token": "[SEP]",
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