ishan/bert-base-uncased-mnli

We used the pretrained model from bert-base-uncased and finetuned it on MultiNLI dataset . The training parameters were kept the same as Devlin et al., 2019 (learning rate = 2e-5, training epochs = 3, epochs . 3, max_sequence_len = 128 and batch_size = 32).Evaluation Results are mentioned in the table below . The evaluation results are mentioned below. The test results are shown below: Matched 0.8456 - mismatched lyrics and rearable accurate accuracy rewarding reassurance rewards ,