Nicolay Rusnachenko

nicolay-r

AI & ML interests

Information Retrieval・Medical Multimodal NLP (🖼+📝) Research Fellow @BU_Research・software developer http://arekit.io・PhD in NLP

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replied to ychen's post about 12 hours ago

Here's some annoying keywords that 4o tends to use when responding to personal experiences with negative sentiments. Will be updated over time. `rough, tough, sound like, sounds like, frustrating, overwhelming`

replied to ychen's post 1 day ago

posted an update 1 day ago

📢 For those who interested in quick extraction of emotion causes in dialogues, below is a notebook that adopts the pre-trained Flan-T5 model on FRIENDS dataset powered by bulk-chain framework: https://gist.github.com/nicolay-r/c8cfe7df1bef0c14f77760fa78ae5b5c Why it might be intersted to check? The provided supports batching mode for a quck inference. In the case of Flan-T5-base that would be the quickest option via LLM. 📊 Evaluation results are available in model card: https://huggingface.co./nicolay-r/flan-t5-emotion-cause-thor-base

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📢 For those who interested in quick extraction of emotion causes in dialogues, below is a notebook that adopts the pre-trained Flan-T5 model on FRIENDS dataset powered by bulk-chain framework:

https://gist.github.com/nicolay-r/c8cfe7df1bef0c14f77760fa78ae5b5c

Why it might be intersted to check? The provided supports batching mode for a quck inference. In the case of Flan-T5-base that would be the quickest option via LLM.

📊 Evaluation results are available in model card:
nicolay-r/flan-t5-emotion-cause-thor-base

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626

📢 Being inspired by effective LLM usage, delighted to share an approach that might boost your reasonging process 🧠 Sharing the demo for interactive launch of Chain-of-Thoght (CoT) schema in bash with the support of [optionally] predefined parameters as input files. The demo demonstrates application for author sentiment extraction towards object in text.

This is a part of the most recent release of the bulk-chain 0.25.0.
⭐ https://github.com/nicolay-r/bulk-chain/releases/tag/0.25.1

How it works: it launches your CoT by asking missed parameters if necessary. For each item of the chain you receive input prompt and streamed output of your LLM.

To settle onto certain parameters, you can pass them via --src:
- TXT files (using filename as a parameter name)
- JSON dictionaries for multiple

🤖 Model: meta-llama/Llama-3.3-70B-Instruct
🌌 Other models: https://github.com/nicolay-r/nlp-thirdgate

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