Update README.md

README.md

@@ -4,7 +4,7 @@ inference:
     do_sample: true
     max_length: 512
     top_p: 0.9
-    repetition_penalty: 1.2
+    repetition_penalty: 1.0
 language: 
   - en
 license: mit
@@ -40,7 +40,7 @@ widget:
 
 **Our [full model](https://huggingface.co/haining/scientific_abstract_simplification) is out!🎉🎉🎉 It leverages the power of multi-instruction finetuning and beats the baseline by a margin. Use the [full model](https://huggingface.co/haining/scientific_abstract_simplification) unless the goal is comparison.**
 
-Scientific Abstract Simplification-baseline *translates* hard-to-read scientific abstracts😵 into more accessible language😇. We hope it can make scientific knowledge accessible for everyone🤗.
+Scientific Abstract Simplification rewrites hard-to-read scientific abstracts😵 into more simpler yet relevant scientific stories😇. We hope our model can make scientific knowledge accessible for everyone🤗.
 
 Try it now with the Hosted inference API on the right.
 You can choose an existing example or paste in any (perhaps full-of-jargon) abstract. Remember to prepend the instruction to the abstract ("summarize, simplify, and contextualize: "; notice, there is a whitespace after the colon). Local use refers to Section [Usage](#Usage).
@@ -145,18 +145,19 @@ Implementations of SacreBLEU, BERT Score, ROUGLE, METEOR, and SARI are from Hugg
 
 ## Results 
 
+We tested our model on the SAS test set (200 samples). We generate 10 lay summaries based on each sample's abstract. During generation, we used top-p sampling with p=0.9. The mean performance is reported below.
+
 | Metrics        | SAS-baseline      |
 |----------------|-------------------|
-| SacreBLEU↑     | 20.97             |
-| BERT Score F1↑ | 0.89              |
-| ROUGLE-1↑      | 0.48              |
-| ROUGLE-2↑      | 0.23              |
-| ROUGLE-L↑      | 0.32              |
-| METEOR↑        | 0.39              |
-| SARI↑          | 46.83             |
-| ARI↓*          | 17.12 (std. 1.97) |
-
-* Note: Half of the generated texts are too short (less than 100 words) to calcualte meaningful ARI. We therefore concatenated adjecent two texts and compute ARI for the 100 texts (instead of original 200 texts).
+| SacreBLEU↑     | 18.43             |
+| BERT Score F1↑ | 89.31             |
+| ROUGLE-1↑      | 48.14             |
+| ROUGLE-2↑      | 22.96             |
+| ROUGLE-L↑      | 32.29             |
+| METEOR↑        | 39.04             |
+| SARI↑          | 46.68             |
+| ARI↓           | 17.27             |
+Note: 1. Some generated texts are too short (less than 100 words) to calcualte meaningful ARI. We therefore concatenated adjecent five texts and compute ARI for the 400 longer texts (instead of original 2,000 texts). 2. BERT score, ROUGE, and METEOR are multiplied by 100.
 
 
 # Contact
@@ -165,7 +166,7 @@ Please [contact us](mailto:hw56@indiana.edu) for any questions or suggestions.
 
 # Disclaimer
 
-The model (sas_baseline) is created for making scientific abstracts more accessible. Its outputs should not be used or trusted outside of its scope. There is no guarantee that the generated text is perfectly aligned with the research. Resort to human experts or original papers when a decision is critical.
+This model is created for making scientific abstracts more accessible. Its outputs should not be used or trusted outside of its scope. There is no guarantee that the generated text is perfectly aligned with the research. Resort to human experts or original papers when a decision is critical.
 
 
 # Acknowledgement