Update README.md

README.md

@@ -3,7 +3,7 @@ language:
 - en
 license: cc-by-nc-sa-4.0
 size_categories:
-- 1B<n<10B
+- 10B<n<100B
 task_categories:
 - other
 pretty_name: ' Video2Reaction '
@@ -81,4 +81,4 @@ Our main contributions are as follows:
  * We introduce **Video2Reaction**, a high-quality dataset that maps movie scenes to distributions over audience reactions, grounded in large-scale real-world viewer comments.
  * We develop a scalable two-stage automatic annotation pipeline that enables cost-effective, extensible reaction labeling—paving the way for future dataset updates as new content and evolving audience perspectives emerge.
  * We propose a novel benchmark task: predicting audience reaction distributions from multimodal video content, and design a comprehensive evaluation framework that captures both distributional alignment and dominant emotional salience.
- * We benchmark a diverse range of approaches—including classical LDL algorithms, adapted multimodal emotion recognition models, and zero-shot foundation vision-language models—highlighting their strengths and limitations in forecasting human reactions towards movie content.
+ * We benchmark a diverse range of approaches—including classical LDL algorithms, adapted multimodal emotion recognition models, and zero-shot foundation vision-language models—highlighting their strengths and limitations in forecasting human reactions towards movie content.