chizhikchi
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sci-five-radsum23

text2text-generation

text-generation-inference

Inference Endpoints

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chizhikchi commited on May 2, 2023

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@@ -26,7 +26,7 @@ This repository provides access to our best-performing system that resulted from
 # Results
 The official evaluation results prove that adaptation of a general-domain system for biomedical literature is beneficial for the subsequent fine-tuning for radiology report summarization task. The Table below summarizes the official scores obtained by this model during the official evaluation. Team standings re available [here](https://vilmedic.app/misc/bionlp23/leaderboard/).
-| BLEU4 | ROUGUEL | BERTscore | F1-RadGraph
 |-----------|--------|----------|----------|
 |   017.38	| 32.32 |	55.04  |	33.96  |

 # Results
 The official evaluation results prove that adaptation of a general-domain system for biomedical literature is beneficial for the subsequent fine-tuning for radiology report summarization task. The Table below summarizes the official scores obtained by this model during the official evaluation. Team standings re available [here](https://vilmedic.app/misc/bionlp23/leaderboard/).
+| BLEU4 | ROUGE-L | BERTscore | F1-RadGraph
 |-----------|--------|----------|----------|
 |   017.38	| 32.32 |	55.04  |	33.96  |