metadata
language:
- en
license: apache-2.0
size_categories:
- 1K<n<10K
source_datasets: tau/scrolls
task_categories:
- text2text-generation
- summarization
tags:
- scrolls
- qmsum
dataset_info:
- config_name: default
features:
- name: id
dtype: string
- name: pid
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: input_token_count
dtype: int64
- name: output_token_count
dtype: int64
splits:
- name: train
num_bytes: 68960760
num_examples: 1257
- name: validation
num_bytes: 15700972
num_examples: 272
- name: test
num_bytes: 16120860
num_examples: 281
download_size: 42316972
dataset_size: 100782592
- config_name: no-prefix
features:
- name: id
dtype: string
- name: pid
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 68944419
num_examples: 1257
- name: validation
num_bytes: 15697436
num_examples: 272
- name: test
num_bytes: 16117207
num_examples: 281
download_size: 6180898
dataset_size: 100759062
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- config_name: no-prefix
data_files:
- split: train
path: no-prefix/train-*
- split: validation
path: no-prefix/validation-*
- split: test
path: no-prefix/test-*
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:
| 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):
token counts
