Update README.md

README.md

@@ -101,6 +101,20 @@ For more details, please refer to the checkpoints linked with the scores. On ove
 | RTE   | Accuracy               | [67.15](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qnli)       | [62.82](https://huggingface.co/gchhablani/fnet-base-finetuned-qnli)       |  63  |04:51         | 03:24    |
 | WNLI  | Accuracy               | [46.48](https://huggingface.co/gchhablani/bert-base-cased-finetuned-wnli)       | [54.93](https://huggingface.co/gchhablani/fnet-base-finetuned-wnli)       |  -  |03:23         | 02:37    |
 
+| Task  | Training time                 | Metric |          |  Result                                                        |                  |                                                                           |
+| ----- | ---------------------- | ------------- | -------- | -------------------------------------------------------------- |----------------- | ------------------------------------------------------------------------- |
+|       |                        |  Bert (PyTorch) - Reproduced           | FNet (PyTorch) - Reproduced     | Bert (PyTorch) - Reproduced                                                                            | FNet (PyTorch) - Reproduced | FNet (Flax) - Official                                                              |
+| MNLI  |  09:52:33      | 06:40:55 |Accuracy or Match/Mismatch              | [84.10](https://huggingface.co/gchhablani/bert-base-cased-finetuned-mnli) (Accuracy)      | [76.75](https://huggingface.co/gchhablani/fnet-base-finetuned-mnli) (Accuracy)      | 72/73 (Match/Mismatch) |
+| QQP   | 09:25:01      | 06:21:16 |mean(Accuracy,F1)            | [89.26](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qqp)  | [86.5](https://huggingface.co/gchhablani/fnet-base-finetuned-qqp)  | 83 | 
+| QNLI  | 02:40:22      | 01:48:22 |Accuracy               | [90.99](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qnli)       | [84.39](https://huggingface.co/gchhablani/fnet-base-finetuned-qnli)       | 80  |
+| SST-2 |  01:42:17      | 01:09:27 | Accuracy               | [92.32](https://huggingface.co/gchhablani/bert-base-cased-finetuned-sst2)       | [89.45](https://huggingface.co/gchhablani/fnet-base-finetuned-sst2)       | 95 |
+| CoLA  |  14:20         | 09:47    | Matthews corr or Accuracy         | [59.57](https://huggingface.co/gchhablani/bert-base-cased-finetuned-cola) (Matthews corr)     | [35.94](https://huggingface.co/gchhablani/fnet-base-finetuned-cola) (Matthews Corr)      | 69 (Accuracy) |
+| STS-B |  10:24         | 07:09    |Spearman corr. | [88.98](https://huggingface.co/gchhablani/bert-base-cased-finetuned-stsb) | [82.19](https://huggingface.co/gchhablani/fnet-base-finetuned-stsb) | 79  |
+| MRPC  | 11:12         | 07:48    | mean(F1/Accuracy)           | [88.15](https://huggingface.co/gchhablani/bert-base-cased-finetuned-mrpc) | [81.15](https://huggingface.co/gchhablani/fnet-base-finetuned-mrpc) |  76 |
+| RTE   | 04:51         | 03:24    | Accuracy               | [67.15](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qnli)       | [62.82](https://huggingface.co/gchhablani/fnet-base-finetuned-qnli)       |  63  |
+| WNLI  | 03:23         | 02:37    |Accuracy               | [46.48](https://huggingface.co/gchhablani/bert-base-cased-finetuned-wnli)       | [54.93](https://huggingface.co/gchhablani/fnet-base-finetuned-wnli)       |  -  |
+
+
 We can see that FNet-base achieves around 93% of BERT-base's performance while it requires *ca.* 30% less time to fine-tune on the downstream tasks.
 
 ### How to use