Add results table

README.md

@@ -19,6 +19,23 @@ The other (non-default) version which can be used is:
 Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by
 the Hugging Face team and contributors.
 
+## Results on SQA - Dev Accuracy
+
+Size     |  Reset  | Dev Accuracy | Link
+-------- | --------| -------- | ----
+LARGE | noreset | 0.7223 | [tapas_sqa_inter_masklm_large.zip](https://huggingface.co/google/tapas-large-finetuned-sqa/tree/no_reset)
+LARGE | reset | 0.7289 | [tapas_sqa_inter_masklm_large_reset.zip](https://huggingface.co/google/tapas-large-finetuned-sqa/tree/main)
+BASE | noreset | 0.6737 | [tapas_sqa_inter_masklm_base.zip](https://huggingface.co/google/tapas-base-finetuned-sqa/tree/no_reset)
+BASE | reset | 0.6874 | [tapas_sqa_inter_masklm_base_reset.zip](https://huggingface.co/google/tapas-base-finetuned-sqa/tree/main)
+MEDIUM | noreset | 0.6464 | [tapas_sqa_inter_masklm_medium.zip]((https://huggingface.co/google/tapas-medium-finetuned-sqa/tree/no_reset))
+MEDIUM | reset | 0.6561 | [tapas_sqa_inter_masklm_medium_reset.zip]((https://huggingface.co/google/tapas-medium-finetuned-sqa/tree/main))
+SMALL | noreset | 0.5876 | [tapas_sqa_inter_masklm_small.zip]((https://huggingface.co/google/tapas-small-finetuned-sqa/tree/no_reset))
+SMALL | reset | 0.6155 | [tapas_sqa_inter_masklm_small_reset.zip]((https://huggingface.co/google/tapas-small-finetuned-sqa/tree/main))
+MINI | noreset | 0.4574 | [tapas_sqa_inter_masklm_mini.zip]((https://huggingface.co/google/tapas-mini-finetuned-sqa/tree/no_reset))
+MINI | reset | 0.5148 | [tapas_sqa_inter_masklm_mini_reset.zip]((https://huggingface.co/google/tapas-mini-finetuned-sqa/tree/main))
+TINY | noreset | 0.2004 | [tapas_sqa_inter_masklm_tiny.zip]((https://huggingface.co/google/tapas-tiny-finetuned-sqa/tree/no_reset))
+TINY | reset | 0.2375 | [tapas_sqa_inter_masklm_tiny_reset.zip]((https://huggingface.co/google/tapas-tiny-finetuned-sqa/tree/main))
+
 ## Model description
 
 TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion.