Bge base en v1.5
Model Overview¶
The BAAI/bge-base-en-v1.5 model is a text embedding model developed by the Beijing Academy of Artificial Intelligence (BAAI), that can map any text to a low-dimensional dense vector for tasks like retrieval, classification, and semantic search. The model can be fine-tuned on your own data to improve its performance on domain-specific tasks.
Model inputs and outputs
Inputs:¶
Text: The bge-base-en model can take any text as input, such as a sentence, paragraph, or document.
Instruction (optional): For text retrieval tasks, the input text can optionally be prefixed with an instruction to improve performance, such as "Represent this sentence for searching relevant passages:".
Outputs:¶
Embedding vector: The model outputs a fixed-size dense vector representation of the input text, which can be used for downstream tasks like retrieval, classification, clustering, or semantic search.
QPC Configurations¶
| Batch Size | SEQUENCE LENGTH | CORES | OLS | Generated URL | Download |
|---|---|---|---|---|---|
| 1 | 512 | 2 | 2 | https://dc00tk1pxen80.cloudfront.net/SDK1.19.6/BAAI/bge-base-en-v1.5/compiled-bin-fp16-B1-C2-A7-best-throughput.tar.gz | Download |