deepset/minilm-uncased-squad2 cover image

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.

Public
$0.0005/sec

Input

question relating to context

question source material

You need to login to use this model

Output

fox (0.18)

 


© 2023 Deep Infra. All rights reserved.

Discord Logo