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t5-v1_1-large-rss

Text2Text Generation Transformers PyTorch

English t5 Inference Endpoints text-generation-inference

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ocastel commited on Aug 20, 2021

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@@ -52,7 +52,7 @@ Evaluated over few-shot QA in a zero-shot setting (no finetuning on annotated ex
 |Model \ Dataset| SQuAD |TriviaQA | NaturalQs | NewsQA | SearchQA | HotpotQA | BioASQ | TextbookQA|
 |:-------------:|:-----:|:-------:|:---------:|:------:|:--------:|:--------:|:------:|:---------:|
-|T5             | 50.4  | 61.7    | 42.1      | 19.2   | 17.8     | 55.5     | 43.3   | 17.8      |
 |T5-rss         | 71.4  | 69.3    | 57.2      | 43.2   | 29.7     | 59.0     | 65.5   | 39.0      |
 The gap between the two models diminishes as more training examples are introduced, for additional result see the [paper]((https://arxiv.org/abs/2108.05857).

 |Model \ Dataset| SQuAD |TriviaQA | NaturalQs | NewsQA | SearchQA | HotpotQA | BioASQ | TextbookQA|
 |:-------------:|:-----:|:-------:|:---------:|:------:|:--------:|:--------:|:------:|:---------:|
+|T5             | 50.4  | 61.7    | 42.1      | 19.2   | 24.0     | 43.3     | 55.5   | 17.8      |
 |T5-rss         | 71.4  | 69.3    | 57.2      | 43.2   | 29.7     | 59.0     | 65.5   | 39.0      |
 The gap between the two models diminishes as more training examples are introduced, for additional result see the [paper]((https://arxiv.org/abs/2108.05857).