language:en size_categories:10K<n<100K format:parquet modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
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language:
- en
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First, I merged instruction and context columns because it's weird to have instructions saying "summarize this" without the passage itself. 

Then I used Senku-70B Q2 GGUF to rate each example out of 10 using a custom-made prompt based on clarity, completeness, correctness, relevance and formatting. Here are a few examples of below 5 pairs:

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6324eabf05bd8a54c6eb1650/kTWL4qWKpadViUgtuu_mp.png)

Observations & Thoughts:

- There 1734 examples with <6.5 score and 562 examples with <5 score. Around 10% of the dataset looks low quality and/or confusing.

- There may be improvement potential with better meta prompt and quantized models.

- It took ~15 hours processing all of the dataset using an RTX 3090.

- Miqu's Fine-Tune Senku did a good job at only providing numeric answers in instruction mode. Rates with alphabetic characters were removed, which were less than a hundred. Most of the removed content were a bit confusing.

Leave a like if human disappear like dinosaur. Also keep in mind that I have no idea what I am doing actually.