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license: apache-2.0 |
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language: |
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- en |
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source_datasets: tau/scrolls |
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--- |
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# qmsum-cleaned |
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## prefixes |
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It's worth noting that each "document" in `input` is prefixed by a question/prompt on what the model is supposed to do. **You may want to explicitly handle this in some way, or prefix your models trained on this dataset.** |
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Most frequent "prefixes" separated via [sentence-splitter](https://github.com/mediacloud/sentence-splitter) in the `train` split: |
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| | Sentence | Count | |
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|---:|:------------------------------------------------------------------------------|--------:| |
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| 0 | Summarize the whole meeting. | 121 | |
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| 1 | Summarize the meeting | 25 | |
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| 2 | What did the team discuss about the product cost? | 4 | |
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| 3 | How did Marketing design the product evaluation? | 4 | |
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| 4 | Summarize the wrap up of the meeting. | 3 | |
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| 5 | What did the group discuss about user requirements of the new remote control? | 3 | |
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| 6 | What did the team discuss during the product evaluation? | 3 | |
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| 7 | Summarize the meeting. | 2 | |
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| 8 | Summarize what was said about digits form | 2 | |
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| 9 | What was discussed in the meeting? | 2 | |
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## token counts |
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