Update README.md

README.md

@@ -100,22 +100,21 @@ The following table summarizes the results for [fnet-base](https://huggingface.c
 | WNLI | [00:02:37](https://huggingface.co/gchhablani/fnet-base-finetuned-wnli) | [00:03:23](https://huggingface.co/gchhablani/bert-base-cased-finetuned-wnli)|
 | SUM | 16:30:45 | 24:23:56 |
 
-
-| Task | MNLI-(m/mm) | QQP  | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE  | WNLI | SUM |
-|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:----:|:-------:|
-|FNet-base (PyTorch)| [06:40:55](https://huggingface.co/gchhablani/fnet-base-finetuned-mnli)| [06:21:16](https://huggingface.co/gchhablani/fnet-base-finetuned-qqp) | [01:48:22](https://huggingface.co/gchhablani/fnet-base-finetuned-qnli) | [01:09:27](https://huggingface.co/gchhablani/fnet-base-finetuned-sst2) | [00:09:47](https://huggingface.co/gchhablani/fnet-base-finetuned-cola) | [00:07:09](https://huggingface.co/gchhablani/fnet-base-finetuned-stsb) | [00:07:48](https://huggingface.co/gchhablani/fnet-base-finetuned-mrpc) | [00:03:24](https://huggingface.co/gchhablani/fnet-base-finetuned-rte) | [00:02:37](https://huggingface.co/gchhablani/fnet-base-finetuned-wnli) | 16:30:45 |
-|Bert-base (PyTorch)| [09:52:33](https://huggingface.co/gchhablani/bert-base-cased-finetuned-mnli)| [09:25:01](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qqp) | [02:40:22](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qnli) | [01:42:17](https://huggingface.co/gchhablani/bert-base-cased-finetuned-sst2) | [00:14:20](https://huggingface.co/gchhablani/bert-base-cased-finetuned-cola) | [00:10:24](https://huggingface.co/gchhablani/bert-base-cased-finetuned-stsb) | [00:11:12](https://huggingface.co/gchhablani/bert-base-cased-finetuned-mrpc) | [00:04:51](https://huggingface.co/gchhablani/bert-base-cased-finetuned-rte) | [00:03:23](https://huggingface.co/gchhablani/bert-base-cased-finetuned-wnli) | 24:23:56 |
-
-On average the PyTorch version of FNet-base requires *ca.* 30% less time for GLUE fine-tuning on GPU.
+On average the PyTorch version of FNet-base requires *ca.* 32% less time for GLUE fine-tuning on GPU.
 
 The following table summarizes the results for [fnet-base](https://huggingface.co/google/fnet-base) (called *FNet (PyTorch) - Reproduced*) and [bert-base-cased](https://hf.co/models/bert-base-cased) (called *Bert (PyTorch) - Reproduced*) in terms of performance and compares it to the reported performance of the official FNet-base model (called *FNet (Flax) - Official*).
 
-| Task | MNLI-(m/mm) | QQP  | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE  | WNLI | Avg |
-|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:----:|:-------:|
-| Metric | Accuracy or Match/Mismatch | mean(Accuracy,F1) | Accuracy | Accuracy | Matthews corr or Accuracy | Spearman corr. |  mean(F1/Accuracy) | Accuracy | Accuracy | - |
-|FNet-base (PyTorch)| [76.75](https://huggingface.co/gchhablani/fnet-base-finetuned-mnli)| [86.5](https://huggingface.co/gchhablani/fnet-base-finetuned-qqp) | [84.39](https://huggingface.co/gchhablani/fnet-base-finetuned-qnli) | [89.45](https://huggingface.co/gchhablani/fnet-base-finetuned-sst2) | [35.94](https://huggingface.co/gchhablani/fnet-base-finetuned-cola) | [82.19](https://huggingface.co/gchhablani/fnet-base-finetuned-stsb) | [81.15](https://huggingface.co/gchhablani/fnet-base-finetuned-mrpc) | [62.82](https://huggingface.co/gchhablani/fnet-base-finetuned-rte) | [54.93](https://huggingface.co/gchhablani/fnet-base-finetuned-wnli) | - |
-|Bert-base (PyTorch)| [84.10](https://huggingface.co/gchhablani/bert-base-cased-finetuned-mnli)| [89.26](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qqp) | [90.99](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qnli) | [92.32](https://huggingface.co/gchhablani/bert-base-cased-finetuned-sst2) | [59.57](https://huggingface.co/gchhablani/bert-base-cased-finetuned-cola) | [88.98](https://huggingface.co/gchhablani/bert-base-cased-finetuned-stsb) | [88.15](https://huggingface.co/gchhablani/bert-base-cased-finetuned-mrpc) | [67.15](https://huggingface.co/gchhablani/bert-base-cased-finetuned-rte) | [46.48](https://huggingface.co/gchhablani/bert-base-cased-finetuned-wnli) | - |
-| FNet-Base (Flax - official) | 72/73 | 83 | 80 | 95 | 69 | 79 | 76 | 63 | - | 76.7 |
+| Task/Model | Metric | FNet-base (PyTorch) | Bert-base (PyTorch) | FNet-Base (Flax - official) |
+| MNLI-(m/mm) | Accuracy or Match/Mismatch  | [76.75](https://huggingface.co/gchhablani/fnet-base-finetuned-mnli) | [84.10](https://huggingface.co/gchhablani/bert-base-cased-finetuned-mnli) | 72/73 |
+| QQP | mean(Accuracy,F1)  | [86.5](https://huggingface.co/gchhablani/fnet-base-finetuned-qqp) | [89.26](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qqp) | 83 |
+| QNLI | Accuracy  | [84.39](https://huggingface.co/gchhablani/fnet-base-finetuned-qnli) | [90.99](https://huggingface.co/gchhablani/bert-base-cased-finetuned-qnli) | 80 | 
+| SST-2 | Accuracy  | [89.45](https://huggingface.co/gchhablani/fnet-base-finetuned-sst2) | [92.32](https://huggingface.co/gchhablani/bert-base-cased-finetuned-sst2) | 95 |
+| CoLA | Matthews corr or Accuracy | [35.94](https://huggingface.co/gchhablani/fnet-base-finetuned-cola) | [59.57](https://huggingface.co/gchhablani/bert-base-cased-finetuned-cola) | 69 |
+| STS-B | Spearman corr. | [82.19](https://huggingface.co/gchhablani/fnet-base-finetuned-stsb) | [88.98](https://huggingface.co/gchhablani/bert-base-cased-finetuned-stsb) | 79 | 
+| MRPC | mean(F1/Accuracy) | [81.15](https://huggingface.co/gchhablani/fnet-base-finetuned-mrpc) | [88.15](https://huggingface.co/gchhablani/bert-base-cased-finetuned-mrpc) | 76 | 
+| RTE | Accuracy | [62.82](https://huggingface.co/gchhablani/fnet-base-finetuned-rte) | [67.15](https://huggingface.co/gchhablani/bert-base-cased-finetuned-rte) | 63 |
+| WNLI | Accuracy | [54.93](https://huggingface.co/gchhablani/fnet-base-finetuned-wnli) | [46.48](https://huggingface.co/gchhablani/bert-base-cased-finetuned-wnli) | - |
+| Avg  | - | - | - | 76.7 |
 
 We can see that FNet-base achieves around 93% of BERT-base's performance on average.