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:
text
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)
text
✅ 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)
text
✅ 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 🔥
text
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: 
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Benchmark: 
https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
link
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Model: 
text
Model: 
https://huggingface.co/microsoft/Phi-3-small-8k-instruct
microsoft/Phi-3-small-8k-instruct
model
resource
https://huggingface.co/microsoft/Phi-3-small-8k-instruct
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Dataset: 
text
Dataset: 
https://github.com/dialogue-evaluation/RuSentNE-evaluation
https://github.com/dialogue-evaluation/RuSentNE-evaluation
link
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Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
text
Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
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Collection: 
text
Collection: 
https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101
https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101
link
new_line
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

👍
Norod78 MaziyarPanahi adamm-hf jyoung105
👀
victor adamm-hf
65f0d05cfd7e9976e3dcfffe
/avatars/c574b7c7ace902c4a613373d3a64e381.svg
Pavan Satish
Pavansatish
user
64e62d11d27a8292c3637f86
https://cdn-avatars.huggingface.co/v1/production/uploads/64e62d11d27a8292c3637f86/aptDeBHpCJxcREj6KPLN1.jpeg
Nicolay Rusnachenko
nicolay-r
user