Update README.md

README.md

@@ -70,35 +70,24 @@ Palmyra's core functionality is to take a string of text and predict the next to
 
 Palmyra was trained on Writer custom data. As with all language models, it is difficult to predict how Palmyra will respond to specific prompts, and offensive content may appear unexpectedly. We recommend that the outputs be curated or filtered by humans before they are released, both to censor undesirable content and to improve the quality of the results.
 
-## Evaluation results
+## Use case
+The fastest model and can perform tasks such as text parsing, address correction, and certain types of classification tasks that do not require a lot of nuance. More context can frequently improve Palmyra-small performance.
 
-Evaluation of Palmyra-small model on the SuperGLUE benchmark
+Good at: Text parsing, simple classification, and keyword recognition are all strengths.
 
 
-|   Task     | Metric | Value |
-|------------|--------|-------|
-|   boolq    |  acc   | 64.43 |
-|   cb       |  acc   | 10.71 |
-|            |  f1    | 08.32 |
-|   copa     |  acc   | 76.00 |
-|   multirc  |  acc   | 01.26 |
-|   record   |  f1    | 84.02 |
-|            |  em    | 83.29 |
-|   wic      |  acc   | 50.00 |
-|   wsc      |  acc   | 36.54 |
-
 
 ## Citation and Related Information
 
 
 To cite this model:
-```bibtex
+```
 @misc{Palmyra,
-  author = {Kiran and Komatsuzaki, Aran},
-  title = {{GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model}},
+  author = {Writer AI team},
+  title = {{Palmyra-small Parameter Autoregressive Language Model}},
   howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
-  year = 2021,
-  month = May
+  year = 2023,
+  month = January 
 }
 ```