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📢 Impressed with the application of the microsoft/Phi-3-mini-4k-instruct (3B) performance in zero-shot-learning (ZSL) mode reasoning 🧠 on Target Sentiment Analysis (TSA) problem. 
📢 Impressed with the application of the microsoft/Phi-3-mini-4k-instruct (3B) performance in zero-shot-learning (ZSL) mode reasoning 🧠 on Target Sentiment Analysis (TSA) problem. 
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💡 There are three major takeaways  out of this experiment 🧪 and they are as follows:
💡 There are three major takeaways  out of this experiment 🧪 and they are as follows:
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✅ 1. Phi-3 slightly outperforms Mistral-7B (official Mistral API, v0.1 or v0.2) on texts written in English
✅ 1. Phi-3 slightly outperforms Mistral-7B (official Mistral API, v0.1 or v0.2) on texts written in English
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✅ 2. Performs similar to LLaMA-3-8B-Instruct on texts translated in English 🔥 
✅ 2. Performs similar to LLaMA-3-8B-Instruct on texts translated in English 🔥 
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☑️ 3. Reasoning in non-english language (🇷🇺) is pretty decent but underperforms to the similar 7B sized models.
☑️ 3. Reasoning in non-english language (🇷🇺) is pretty decent but underperforms to the similar 7B sized models.
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This is a huge step forward since release of Phi-2, especially because the predecessor (microsoft/phi-2) was not capable for performing reasoning in non-english texts (🇷🇺) at all!
This is a huge step forward since release of Phi-2, especially because the predecessor (microsoft/phi-2) was not capable for performing reasoning in non-english texts (🇷🇺) at all!
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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-mini-4k-instruct
model
microsoft/Phi-3-mini-4k-instruct
https://huggingface.co/microsoft/Phi-3-mini-4k-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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📢 Impressed with the application of the microsoft/Phi-3-mini-4k-instruct (3B) performance in zero-shot-learning (ZSL) mode reasoning 🧠 on Target Sentiment Analysis (TSA) problem. 
💡 There are three major takeaways  out of this experiment 🧪 and they are as follows:
✅ 1. Phi-3 slightly outperforms Mistral-7B (official Mistral API, v0.1 or v0.2) on texts written in English
✅ 2. Performs similar to LLaMA-3-8B-Instruct on texts translated in English 🔥 
☑️ 3. Reasoning in non-english language (🇷🇺) is pretty decent but underperforms to the similar 7B sized models.

This is a huge step forward since release of Phi-2, especially because the predecessor (microsoft/phi-2) was not capable for performing reasoning in non-english texts (🇷🇺) at all!

Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Model: https://huggingface.co/microsoft/Phi-3-mini-4k-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

🔥
victor Taylor658 ithonestman kristaller486 lunarflu hiauiarau
👀
osanseviero ithonestman lunarflu
/avatars/7fa9de162694d34a214ccd8ecb02fa0a.svg
Sergey Zubrilin
hiauiarau
user
https://cdn-avatars.huggingface.co/v1/production/uploads/64e62d11d27a8292c3637f86/aptDeBHpCJxcREj6KPLN1.jpeg
Nicolay Rusnachenko
nicolay-r
user