shibing624/text2vec-base-chinese
A sentence similarity model that can be used for various NLP tasks such as text classification, sentiment analysis, named entity recognition, question answering, and more. It utilizes the CoSENT architecture, which consists of a transformer encoder and a pooling module, to encode input texts into vectors that capture their semantic meaning. The model was trained on the nli_zh dataset and achieved high performance on various benchmark datasets.
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.
