model description and paper/repo links

README.md

@@ -8,4 +8,28 @@ library_name: transformers
 pipeline_tag: text2text-generation
 tags:
 - code
----
+---
+
+# CodeTIDAL5
+
+We present CodeTIDAL5, a model for type inference on untyped TypeScript / JavaScript!  
+The model was introduced as part of the paper
+
+[_Learning Type Inference for Enhanced Dataflow Analysis_](https://davidbakereffendi.github.io/assets/pdf/preprint_6676_ESORICS23.pdf)  
+Lukas Seidel, Sedick David Baker Effendi, Xavier Pinho, Konrad Rieck, Brink van der Merwe and Fabian Yamaguchi  
+ESORICS 2023  
+
+From the abstract:  
+We propose CodeTIDAL5, a Transformer-based model trained to reliably
+predict type annotations. For effective result retrieval and re-integration,
+we extract usage slices from a program’s code property graph.  
+Comparing our approach against recent neural type inference systems, our
+model outperforms the current state-of-the-art by 7.85% on the ManyTypes4TypeScript benchmark, achieving 71.27% accuracy overall.
+
+## Intended Use
+The model was designed for use with the code analysis platform [Joern](https://github.com/joernio/joern).  
+As part of the paper, we devise a system which seemlessly integrates type inference recommendations from the CodeTIDAL5 model in Joern's 
+Code Property Graphs (CPGs) for enriched context information, aiming at improved taint tracking and dataflow analysis.
+
+An implementation of this approach can be found in the paper's artifact repository:  
+https://github.com/joernio/joernti-codetidal5