deepset/minilm-uncased-squad2
Microsoft's MiniLM-L12-H384-uncased language model achieved state-of-the-art results on the SQuAD 2.0 question-answering benchmark, with exact match and F1 scores of 76.13% and 79.54%, respectively. The model was trained on the SQuAD 2.0 dataset using a batch size of 12, learning rate of 4e-5, and 4 epochs. The authors suggest using their model as a starting point for building large language models for downstream NLP tasks.
Microsoft's MiniLM-L12-H384-uncased language model achieved state-of-the-art results on the SQuAD 2.0 question-answering benchmark, with exact match and F1 scores of 76.13% and 79.54%, respectively. The model was trained on the SQuAD 2.0 dataset using a batch size of 12, learning rate of 4e-5, and 4 epochs. The authors suggest using their model as a starting point for building large language models for downstream NLP tasks.
HTTP/cURL API
Input fields
webhookfile
The webhook to call when inference is done, by default you will get the output in the response of your inference request
