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") - Inference
- Notebooks
- Google Colab
- Kaggle
Update tokenizer_config.json
#12
by freaksamael - opened
No description provided.
For English is not need for Chinese chrs; also, if possible, it will be great if the size is increased; in general we need to tokenize some relevant text in 1024 window. Thank you!
Hi, thanks for your interest.
The max length is 512 during the training, so it cannot process the sequence whose length is larger than 512. Actually, it only uses the first 512 tokens and ignores other tokens.
Therefore, increasing the size of max length has no impact, and the model still only uses the first 512 tokens.