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.
Try the model with a sample request using the same API shape shown in the reference page.
You can add more items with the button on the right
The service tier used for processing the request. When set to 'priority', the request will be processed with higher priority (only applies to models that support it).
Enter a JSON array
whether to normalize the computed embeddings
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. (Default: empty, 32 ≤ dimensions ≤ 8192)
Adjust settings and run the demo to see the model output generated here in real-time.
