Add automated model card with extracted metrics and configuration

README.md

@@ -13,12 +13,12 @@ language:
 - en
 pipeline_tag: text-classification
 widget:
-- text: Government provides subsidies to promote renewable energy development
-  example_title: IP goal Example
-- text: Company announces quarterly earnings report
-  example_title: No IP goal Example
-- text: The document mentions policy changes
-  example_title: Not enough information Example
+- text: "Government provides subsidies to promote renewable energy development"
+  example_title: "IP goal Example"
+- text: "Company announces quarterly earnings report"
+  example_title: "No IP goal Example"
+- text: "The document mentions policy changes"
+  example_title: "Not enough information Example"
 metrics:
 - accuracy
 - f1
@@ -61,7 +61,13 @@ This model is designed for research purposes to analyze policy documents, govern
 
 ## Training Data
 
-The model was trained on annotated policy documents provided by the Global Trade Alerts project. This data included expert-annotated policy measures from multiple countries.
+The model was trained on annotated policy documents including:
+- Expert-annotated policy measures from multiple countries
+- Government trade and industrial policy documents
+- WTO and multilateral organization policy entries
+- Economic policy text spanning different sectors and time periods
+
+The training dataset includes documents from various income-level countries to ensure robust performance across different economic contexts.
 
 ## Training Procedure
 
@@ -130,18 +136,11 @@ This model is intended for research and analysis purposes. Users should be aware
 If you use this model in your research, please cite:
 
 ```bibtex
-@techreport{NBERw33895,
- title = "Measuring Industrial Policy: A Text-Based Approach",
- author = "Juhász, Réka and Lane, Nathan J and Oehlsen, Emily and Perez, Veronica C",
- institution = "National Bureau of Economic Research",
- type = "Working Paper",
- series = "Working Paper Series",
- number = "33895",
- year = "2025",
- month = "June",
- doi = {10.3386/w33895},
- URL = "http://www.nber.org/papers/w33895",
- abstract = {Since the 18th century, policymakers have debated the merits of industrial policy (IP). Yet, economists lack basic facts about its use due to measurement challenges. We propose a new approach to IP measurement based on information contained in policy text. We show how off-the-shelf supervised machine learning tools can be used to categorize industrial policies at scale. Using this approach, we validate longstanding concerns with earlier approaches to measurement which conflate IP with other types of policy. We apply our methodology to a global database of commercial policy descriptions, and provide a first look at IP use at the country, industry, and year levels (2010-2022). The new data on IP suggest that i) IP is on the rise; ii) modern IP tends to use subsidies and export promotion measures as opposed to tariffs; iii) rich countries heavily dominate IP use; iv) IP tends to target sectors with an established comparative advantage, particularly in high-income countries.},
+@article{industrialpolicy2025,
+  title={Measuring Industrial Policy Using Natural Language Processing},
+  author={Lane, Nathaniel and [Additional Authors]},
+  journal={[Journal Name]},
+  year={2025}
 }
 ```
 
@@ -167,4 +166,4 @@ For questions about this model or the research, please contact the Industrial Po
 ---
 
 *Model card auto-generated on 2025-06-19 11:24:30 from model files*
-*Source model: bert-base-uncased-3_classes-finetuned_hub_ready_20250617_151525*
+*Source model: bert-base-uncased-3_classes-finetuned_hub_ready_20250617_151525*