Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10M<n<100M
License:
File size: 1,947 Bytes
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---
language:
- en
license: apache-2.0
source_datasets: vivym/midjourney-messages
task_categories:
- text-generation
dataset_info:
- config_name: deduped
features:
- name: id
dtype: string
- name: channel_id
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2538669745.0
num_examples: 14828769
download_size: 1585207687
dataset_size: 2538669745.0
- config_name: default
features:
- name: id
dtype: string
- name: channel_id
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 3575844717.3610477
num_examples: 19716685
download_size: 1514418407
dataset_size: 3575844717.3610477
configs:
- config_name: deduped
data_files:
- split: train
path: deduped/train-*
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- midjourney
---
# midjourney-messages-cleaned
This is [vivym/midjourney-messages](https://huggingface.co/datasets/vivym/midjourney-messages) but with the following cleaning steps:
- remove most columns (keep `id` columns for reference vs. original)
- Apply `clean-text` to all rows (_keep casing_)
- rename `content` to `text` (ffs)
- remove intermediate ID/tag (???) in angle brackets at the end, remove double asterisks `**`
- remove exact duplicate rows
## dataset structure
overall:
```python
DatasetDict({
train: Dataset({
features: ['id', 'channel_id', 'text'],
num_rows: 19738964
})
})
```
A single example looks like this:
```python
random.choice(dataset['train'])
{'id': '1108635049391308879',
'channel_id': '1008571088919343124',
'text': 'Warhammer 40k Chaos Space Marine with pink Armor and a guitar'}
```
## details
585M GPT-4 tiktoken tokens.
```
token_count
count 1.971668e+07
mean 2.971651e+01
std 3.875208e+01
min 1.000000e+00
25% 1.000000e+01
50% 1.900000e+01
75% 3.400000e+01
max 2.077000e+03
```
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