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