sentence-transformers/all-MiniLM-L6-v2
We present a sentence transformation model that achieves state-of-the-art results on various NLP tasks without requiring task-specific architectures or fine-tuning. Our approach leverages contrastive learning and utilizes a variety of datasets to learn robust sentence representations. We evaluate our model on several benchmarks and demonstrate its effectiveness in various applications such as text classification, sentiment analysis, named entity recognition, and question answering.
We present a sentence transformation model that achieves state-of-the-art results on various NLP tasks without requiring task-specific architectures or fine-tuning. Our approach leverages contrastive learning and utilizes a variety of datasets to learn robust sentence representations. We evaluate our model on several benchmarks and demonstrate its effectiveness in various applications such as text classification, sentiment analysis, named entity recognition, and question answering.
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": "sentence-transformers/all-MiniLM-L6-v2",
"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": "sentence-transformers/all-MiniLM-L6-v2",
"usage": {
"prompt_tokens":12,
"total_tokens":12
}
}
Input fields
service_tierstring
The service tier used for processing the request. When set to 'priority', the request will be processed with higher priority.
Allowed values: defaultpriority
dimensionsinteger
The number of dimensions in the embedding. If not provided, the model's default will be used.If provided bigger than model's default, the embedding will be padded with zeros.
Range: 32 ≤ dimensions
Input Schema
Output Schema
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