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
instruction for other use cases like intent classification
#5
by SudipThomas - opened
What should be the instructions for use cases like intent classification where we have to find embeddings of intent name, intent description, intent example sentences and the unknown sentence for which the intent needs to be found out using cosine similarity?
Are there specific instructions which were used to the train the model for different tasks?
The instruction is used only when we need to find a long description for a short sentence. And we only have one instruction for the short sentence: "Represent this sentence for searching relevant passages: ". Any other instruction is not used in training. For intent classification, there is no need to add instruction.