Update README.md

README.md

@@ -154,7 +154,7 @@ For comparison, this model (ported to PyTorch) was fine-tuned and evaluated usin
 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. A sequence length of 512 was used with batch size 16 and learning rate 2e-5.
 
 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*) both in terms of performance and training times and compares it to the reported performance of the official FNet-base model (called *FNet (Flax) - Official*).
-For more details, please refer to the checkpoints linked with the scores. The sequence length used for 512 with batch size 16 and learning rate 2e-5.
+For more details, please refer to the checkpoints linked with the scores. On overview of all fine-tuned checkpoints of the following table can be accessed [here](https://huggingface.co/models?other=fnet-bert-base-comparison).
  
 | Task  | Metric                 | Result                                                        |                  |                                                                           | Training time |          |
 | ----- | ---------------------- | --------------------------------------------------------------|----------------- | ------------------------------------------------------------------------- | ------------- | -------- |