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by seinvak
175 days ago
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> Would love to learn more about how this is built. I remember a similar project from 4 years ago[0] that used a classic BERT model for NER on HN comments Yes, I saw that project pretty impressive! Hand-labeling 4000 books is definitely not an easy task, mad-respect to tracyhenry for the passion and hardwork that was required back then. For my project, I just used the Gemini 2.5 Flash API (since I had free credits) with the following prompt: """You are an expert literary assistant parsing Hacker News comments.
Rules:
1. Only extract CLEARLY identifiable books.
2. Ignore generic mentions.
3. Return JSON ARRAY only.
4. If no books found, return [].
5. A score from -10 to 10 where 10 is highly recommended, -10 is very poorly recommended and 0 is neutral.
6. If the author's name is in the comment, include it; otherwise, omit the key.
JSON format:
[
{{
"title": "book title",
"sentiment": "score",
"author" : "Name of author if mentioned"
}}
]
Text:
{text}""" It did the job quite well. It really shows how far AI has come in just 4 years. |
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