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📢 Releasing the Chain-of-Thought (CoT)-tuned 🔥 FlanT5-xl (3B) for Target Sentiment Analysis (TSA) on english texts. 
📢 Releasing the Chain-of-Thought (CoT)-tuned 🔥 FlanT5-xl (3B) for Target Sentiment Analysis (TSA) on english texts. 
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💡 The main reason for adopting this model or smaller version (large and base) are as follows:
💡 The main reason for adopting this model or smaller version (large and base) are as follows:
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✅ 1. Reasoning in sentiment-analysis in zero-shot-learning mode significantly underperforms the fine-tuned FlanT5. 
✅ 1. Reasoning in sentiment-analysis in zero-shot-learning mode significantly underperforms the fine-tuned FlanT5. 
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✅ 2. This model showcases top 1 🏆 on the RuSentNE-2023 competitions: 
✅ 2. This model showcases top 1 🏆 on the RuSentNE-2023 competitions: 
link
https://codalab.lisn.upsaclay.fr/competitions/9538
https://codalab.lisn.upsaclay.fr/competitions/9538
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✅ 3. Easy colab for frameworkless lauch and experiments 🧪 
✅ 3. Easy colab for frameworkless lauch and experiments 🧪 
link
https://colab.research.google.com/github/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/FlanT5_Finetuned_Model_Usage.ipynb
https://colab.research.google.com/github/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/FlanT5_Finetuned_Model_Usage.ipynb
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You may find more on the model card, while the fine-tuning statistics per each model size is shown in attachment.
You may find more on the model card, while the fine-tuning statistics per each model size is shown in attachment.
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Model: 
Model: 
resource
https://huggingface.co/nicolay-r/flan-t5-tsa-thor-xl
model
nicolay-r/flan-t5-tsa-thor-xl
https://huggingface.co/nicolay-r/flan-t5-tsa-thor-xl
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Benchmark: 
Benchmark: 
link
https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
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Dataset: 
Dataset: 
link
https://github.com/dialogue-evaluation/RuSentNE-evaluation
https://github.com/dialogue-evaluation/RuSentNE-evaluation
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Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
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Collection: 
Collection: 
link
https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101
https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101
📢 Releasing the Chain-of-Thought (CoT)-tuned 🔥 FlanT5-xl (3B) for Target Sentiment Analysis (TSA) on english texts. 
💡 The main reason for adopting this model or smaller version (large and base) are as follows:
✅ 1. Reasoning in sentiment-analysis in zero-shot-learning mode significantly underperforms the fine-tuned FlanT5. 
✅ 2. This model showcases top 1 🏆 on the RuSentNE-2023 competitions: https://codalab.lisn.upsaclay.fr/competitions/9538
✅ 3. Easy colab for frameworkless lauch and experiments 🧪 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: https://huggingface.co/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
👍
victor naadel23 GPT007 den0620 osanseviero