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The application of  Phi-3-small-8k-instruct  for reasoning  in  Target Sentiment Analysis (TSA), in a  zero-shot-learning mode.  Comparing with the other 7B vendors, the key takeaways are as follows:
The application of  Phi-3-small-8k-instruct  for reasoning  in  Target Sentiment Analysis (TSA), in a  zero-shot-learning mode.  Comparing with the other 7B vendors, the key takeaways are as follows:
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✅ 1.  At the moment this model on the top 🎉 of the 7B sized versions for texts translated in English (🇺🇸) by surpassing Mistral-7B-v0.3 and LLaMA-3-8B 🔥 (Figure 1)
✅ 1.  At the moment this model on the top 🎉 of the 7B sized versions for texts translated in English (🇺🇸) by surpassing Mistral-7B-v0.3 and LLaMA-3-8B 🔥 (Figure 1)
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✅ 2. It remains similar to 7B alternatives in original non-english texts (🇷🇺),  however show confidence in sentiment presence among other 7B alternatives (checkout F1(PN0) results in Figure 2)
✅ 2. It remains similar to 7B alternatives in original non-english texts (🇷🇺),  however show confidence in sentiment presence among other 7B alternatives (checkout F1(PN0) results in Figure 2)
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In comparison with its mini (3B) brother  Phi-3-mini, the small (7B) version showcases a huge step in reasoning capabilities 🔥
In comparison with its mini (3B) brother  Phi-3-mini, the small (7B) version showcases a huge step in reasoning capabilities 🔥
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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/microsoft/Phi-3-small-8k-instruct
model
microsoft/Phi-3-small-8k-instruct
https://huggingface.co/microsoft/Phi-3-small-8k-instruct
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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
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The application of  Phi-3-small-8k-instruct  for reasoning  in  Target Sentiment Analysis (TSA), in a  zero-shot-learning mode.  Comparing with the other 7B vendors, the key takeaways are as follows:
✅ 1.  At the moment this model on the top 🎉 of the 7B sized versions for texts translated in English (🇺🇸) by surpassing Mistral-7B-v0.3 and LLaMA-3-8B 🔥 (Figure 1)
✅ 2. It remains similar to 7B alternatives in original non-english texts (🇷🇺),  however show confidence in sentiment presence among other 7B alternatives (checkout F1(PN0) results in Figure 2)

In comparison with its mini (3B) brother  Phi-3-mini, the small (7B) version showcases a huge step in reasoning capabilities 🔥

Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Model: https://huggingface.co/microsoft/Phi-3-small-8k-instruct
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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Norod78 MaziyarPanahi lunarflu jyoung105
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victor lunarflu
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Pavan Satish
Pavansatish
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Nicolay Rusnachenko
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