# 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)
task_categories:text2text-generation task_categories:summarization size_categories:1K<n<10K source_datasets:tau/scrolls language:en license:apache-2.0 scrolls qmsum region:us
{"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "source_datasets": "tau/scrolls", "task_categories": ["text2text-generation", "summarization"], "tags": ["scrolls", "qmsum"]}
TAGS
#task_categories-text2text-generation #task_categories-summarization #size_categories-1K<n<10K #source_datasets-tau/scrolls #language-English #license-apache-2.0 #scrolls #qmsum #region-us 

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 in the 'train' split:



### wordcloud


Visualized as a wordcloud ('train' split):


!wc


token counts
------------


!counts



### wordcloud


Visualized as a wordcloud ('train' split):


!wc


token counts
------------


!counts
TAGS
#task_categories-text2text-generation #task_categories-summarization #size_categories-1K<n<10K #source_datasets-tau/scrolls #language-English #license-apache-2.0 #scrolls #qmsum #region-us 
 ### wordcloud


Visualized as a wordcloud ('train' split):


!wc


token counts
------------


!counts
passage: TAGS
#task_categories-text2text-generation #task_categories-summarization #size_categories-1K<n<10K #source_datasets-tau/scrolls #language-English #license-apache-2.0 #scrolls #qmsum #region-us 
### wordcloud


Visualized as a wordcloud ('train' split):


!wc


token counts
------------


!counts