source_datasets:tau/scrolls language:en license:apache-2.0 region:us
---
license: apache-2.0
language:
- en
source_datasets: tau/scrolls
---
# qmsum-cleaned



## prefixes


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.**


Most frequent "prefixes" separated via [sentence-splitter](https://github.com/mediacloud/sentence-splitter) in the `train` split:

|    | Sentence                                                                      |   Count |
|---:|:------------------------------------------------------------------------------|--------:|
|  0 | Summarize the whole meeting.                                                  |     121 |
|  1 | Summarize the meeting                                                         |      25 |
|  2 | What did the team discuss about the product cost?                             |       4 |
|  3 | How did Marketing design the product evaluation?                              |       4 |
|  4 | Summarize the wrap up of the meeting.                                         |       3 |
|  5 | What did the group discuss about user requirements of the new remote control? |       3 |
|  6 | What did the team discuss during the product evaluation?                      |       3 |
|  7 | Summarize the meeting.                                                        |       2 |
|  8 | Summarize what was said about digits form                                     |       2 |
|  9 | What was discussed in the meeting?                                            |       2 |

### wordcloud

Visualized as a wordcloud (`train` split):

![wc](prefix-train-wordcloud.png)

## token counts

![counts](https://i.imgur.com/rARAOvr.png)
