thenlper/gte-large
The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc.
The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc.
OpenAI-compatible HTTP API
DeepInfra supports the OpenAI embeddings API. The following creates an embedding vector representing the input text
curl "https://api.deepinfra.com/v1/openai/embeddings" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $DEEPINFRA_TOKEN" \
-d '{
"input": "The food was delicious and the waiter...",
"model": "thenlper/gte-large",
"encoding_format": "float"
}'
which will return something similar to
{
"object":"list",
"data":[
{
"object": "embedding",
"index":0,
"embedding":[
-0.010480394586920738,
-0.0026091758627444506
...
0.031979579478502274,
0.02021978422999382
]
}
],
"model": "thenlper/gte-large",
"usage": {
"prompt_tokens":12,
"total_tokens":12
}
}
