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@@ -157,10 +157,10 @@ According to [the official paper](https://arxiv.org/abs/2105.03824) (*cf.* with
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  The following table contains test results on the HuggingFace model in comparison with [bert-base-cased](https://hf.co/models/bert-base-cased). The training was done on a single 16GB NVIDIA Tesla V100 GPU. For MRPC/WNLI, the models were trained for 5 epochs, while for other tasks, the models were trained for 3 epochs. Please refer to the checkpoints linked with the scores. The sequence length used for 512 with batch size 16 and learning rate 2e-5.
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- | Task | Metric | Result | | Training time | |
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  | ----- | ---------------------- | ------------------------------------------------------------------------------- | ------------------------------------------------------------------------- | ------------- | -------- |
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  | | | Bert (PyTorch) - Reproduced | FNet (PyTorch) - Reproduced | FNet (Flax) - Official | Bert | FNet |
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- | MNLI | Accuracy | [84.10](https://huggingface.co/gchhablani/bert-base-cased-finetuned-mnli) | [76.75](https://huggingface.co/gchhablani/fnet-base-finetuned-mnli) | | 09:52:33 | 06:40:55 |
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  | QQP | mean(Accuracy,F1) | [89.26](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qqp) | [86.5](https://huggingface.co/gchhablani/fnet-base-finetuned-qqp) | | 09:25:01 | 06:21:16 |
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  | QNLI | Accuracy | [90.99](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qnli) | [84.39](https://huggingface.co/gchhablani/fnet-base-finetuned-qnli) | |02:40:22 | 01:48:22 |
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  | SST-2 | Accuracy | [92.32](https://huggingface.co/gchhablani/bert-base-cased-finetuned-sst2) | [89.45](https://huggingface.co/gchhablani/fnet-base-finetuned-sst2) | | 01:42:17 | 01:09:27 |
 
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  The following table contains test results on the HuggingFace model in comparison with [bert-base-cased](https://hf.co/models/bert-base-cased). The training was done on a single 16GB NVIDIA Tesla V100 GPU. For MRPC/WNLI, the models were trained for 5 epochs, while for other tasks, the models were trained for 3 epochs. Please refer to the checkpoints linked with the scores. The sequence length used for 512 with batch size 16 and learning rate 2e-5.
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+ | Task | Metric | Result | | | Training time | |
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  | ----- | ---------------------- | ------------------------------------------------------------------------------- | ------------------------------------------------------------------------- | ------------- | -------- |
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  | | | Bert (PyTorch) - Reproduced | FNet (PyTorch) - Reproduced | FNet (Flax) - Official | Bert | FNet |
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+ | MNLI | Accuracy | [84.10](https://huggingface.co/gchhablani/bert-base-cased-finetuned-mnli) | [76.75](https://huggingface.co/gchhablani/fnet-base-finetuned-mnli) | 72/73 (Match/Mismatch) | 09:52:33 | 06:40:55 |
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  | QQP | mean(Accuracy,F1) | [89.26](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qqp) | [86.5](https://huggingface.co/gchhablani/fnet-base-finetuned-qqp) | | 09:25:01 | 06:21:16 |
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  | QNLI | Accuracy | [90.99](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qnli) | [84.39](https://huggingface.co/gchhablani/fnet-base-finetuned-qnli) | |02:40:22 | 01:48:22 |
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  | SST-2 | Accuracy | [92.32](https://huggingface.co/gchhablani/bert-base-cased-finetuned-sst2) | [89.45](https://huggingface.co/gchhablani/fnet-base-finetuned-sst2) | | 01:42:17 | 01:09:27 |