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📢 The most recent Mistral-7B-Instruct-v0.3 release showcases more robust advances in zero-shot-mode mode on Target Sentiment Analysis.
📢 The most recent Mistral-7B-Instruct-v0.3 release showcases more robust advances in zero-shot-mode mode on Target Sentiment Analysis.
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🧪  We experiment with the original texts (🇷🇺 ) and their translated version into English (🇺🇸).
🧪  We experiment with the original texts (🇷🇺 ) and their translated version into English (🇺🇸).
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💡 The key takeaways on the expectation from this model are as follows:
💡 The key takeaways on the expectation from this model are as follows:
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✔️ 1. On translated texts into English (🇺🇸), it surpasses LLaMA-3 and and nearly touches MOE Mixtral 8x7B versions being quite precise by F1 across all the classes by F1(PN)
✔️ 1. On translated texts into English (🇺🇸), it surpasses LLaMA-3 and and nearly touches MOE Mixtral 8x7B versions being quite precise by F1 across all the classes by F1(PN)
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✔️2. On orignal texts (🇷🇺) It slightly surpasses LLaMA-3 by F1(PN) by being less tolerant in neutral (F1(PN0)).  Using larger versions (Mixtral) are still preferable choice for reasoning 🧠 in non-eng texts.
✔️2. On orignal texts (🇷🇺) It slightly surpasses LLaMA-3 by F1(PN) by being less tolerant in neutral (F1(PN0)).  Using larger versions (Mixtral) are still preferable choice for reasoning 🧠 in non-eng texts.
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✔️3. You can clearly see the difference between 7B version and MOE (figure 3) by F1(PN0)
✔️3. You can clearly see the difference between 7B version and MOE (figure 3) by F1(PN0)
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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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Model: 
Model: 
resource
https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3
model
mistralai/Mistral-7B-Instruct-v0.3
https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3
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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
📢 The most recent Mistral-7B-Instruct-v0.3 release showcases more robust advances in zero-shot-mode mode on Target Sentiment Analysis.
🧪  We experiment with the original texts (🇷🇺 ) and their translated version into English (🇺🇸).
💡 The key takeaways on the expectation from this model are as follows:
✔️ 1. On translated texts into English (🇺🇸), it surpasses LLaMA-3 and and nearly touches MOE Mixtral 8x7B versions being quite precise by F1 across all the classes by F1(PN)
✔️2. On orignal texts (🇷🇺) It slightly surpasses LLaMA-3 by F1(PN) by being less tolerant in neutral (F1(PN0)).  Using larger versions (Mixtral) are still preferable choice for reasoning 🧠 in non-eng texts.
✔️3. You can clearly see the difference between 7B version and MOE (figure 3) by F1(PN0)
Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Model: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3
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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