Datasets:
File size: 2,400 Bytes
94e19f0 76674ef 94e19f0 76674ef |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 100K<n<1M
---
# RedPajama -- Book (refined by Data-Juicer)
A refined version of Book dataset in RedPajama by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/redpajama-book-refine-result.jsonl) (About 91GB).
## Dataset Information
- Number of samples: 195,983 (Keep ~95.51% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-book'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter:
tokenization: false
min_ratio: 0.55 # <3sigma (0.697)
max_ratio: 0.854 # 3sigma
- average_line_length_filter: # for code
max_len: 500 # >3sigma (364)
- character_repetition_filter:
rep_len: 10
max_ratio: 0.2 # >3sigma (0.12)
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.00047 # 3sigma
- language_id_score_filter: # remove language filter
min_score: 0.2
- maximum_line_length_filter: # for code
max_len: 13381 # 3sigma
- perplexity_filter:
lang: en
max_ppl: 6000 # <3sigma (16516)
- special_characters_filter:
max_ratio: 0.5 # >3sigma (0.32)
- words_num_filter:
lang: en
tokenization: true
min_num: 1000
max_num: 539754 # 3sigma
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.194 # 3sigma
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
``` |