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wassname/ultrachat_200k_filtered

Name: wassname/ultrachat_200k_filtered
Creator: wassname
Published: 2025-03-11T08:06:23

published by @wassname

Alignment dataset tracked in the /datasets sub-catalog.

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1.1K

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Size

833.2 MB

Created on HF

2025-03-11

Age

1 years ago

Description

I used a list of filtered words from

https://huggingface.co/datasets/branles14/ultrachat-uncensored/raw/main/filters.txt

https://github.com/NousResearch/finetuning-subnet/blob/e2f5eb6373a417fc7c03e38adbc6cf145670ab58/finetune/dataset.py#L60

to filter ultrachat_200k it's now around 100k

README current version from Hugging Face

dataset_info:
features:

name: prompt
dtype: string
name: prompt_id
dtype: string
name: messages
list:
- name: content
  dtype: string
- name: role
  dtype: string
  splits:
name: train_sft
num_bytes: 791551136.941005
num_examples: 117773
name: test_sft
num_bytes: 88278942.0550411
num_examples: 13188
name: train_gen
num_bytes: 840181104.0518842
num_examples: 159651
name: test_gen
num_bytes: 91907705.81599773
num_examples: 17544
download_size: 873710939
dataset_size: 1811918888.863928
configs:
config_name: default
data_files:
- split: train_sft
  path: data/train_sft-*
- split: test_sft
  path: data/test_sft-*
- split: train_gen
  path: data/train_gen-*
- split: test_gen
  path: data/test_gen-*

I used a list of filtered words from

README history 5 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

2025-06-02Upload datasetd2d77591.2 KB
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2025-03-11Update README.mdfe9c65f810 B
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2025-03-11Update README.md26e53a1741 B
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2025-03-11Upload dataset774867a454 B
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2025-03-11initial commita0de7a20 B
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Used by abliterated models none yet

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Repository

DIR data 6 files FILE .gitattributes FILE README.md

File contents live on Hugging Face. Click a file or folder to open it there.

Download the dataset

Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)

from datasets import load_dataset
ds = load_dataset("wassname/ultrachat_200k_filtered")

Raw snapshot (Python)

from huggingface_hub import snapshot_download
snapshot_download(repo_id="wassname/ultrachat_200k_filtered", repo_type="dataset")

git clone (requires git-lfs)

git clone https://huggingface.co/datasets/wassname/ultrachat_200k_filtered

Browse repository on Hugging Face ->