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nicolay-rΒ 
posted an update Jun 7
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πŸ“’ Releasing the Chain-of-Thought (CoT)-tuned πŸ”₯ FlanT5-xl (3B) for Target Sentiment Analysis (TSA) on english texts.
πŸ’‘ The main reason for adopting this model or smaller version (large and base) are as follows:
βœ… 1. Reasoning in sentiment-analysis in zero-shot-learning mode significantly underperforms the fine-tuned FlanT5.
βœ… 2. This model showcases top 1 πŸ† on the RuSentNE-2023 competitions: https://codalab.lisn.upsaclay.fr/competitions/9538
βœ… 3. Easy colab for frameworkless lauch and experiments πŸ§ͺ https://colab.research.google.com/github/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/FlanT5_Finetuned_Model_Usage.ipynb

You may find more on the model card, while the fine-tuning statistics per each model size is shown in attachment.

Model: nicolay-r/flan-t5-tsa-thor-xl
Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation
Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
Collection: https://huggingface.co./collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101
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