Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
Portuguese
Size:
10K - 100K
ArXiv:
License:
dataset_info: | |
features: | |
- name: prompt | |
dtype: string | |
- name: prompt_id | |
dtype: string | |
- name: messages | |
list: | |
- name: content | |
dtype: string | |
- name: role | |
dtype: string | |
- name: category | |
dtype: string | |
- name: text | |
dtype: string | |
splits: | |
- name: train | |
num_bytes: 30628039 | |
num_examples: 9500 | |
- name: test | |
num_bytes: 1644450 | |
num_examples: 500 | |
download_size: 19873853 | |
dataset_size: 32272489 | |
configs: | |
- config_name: default | |
data_files: | |
- split: train | |
path: data/train-* | |
- split: test | |
path: data/test-* | |
task_categories: | |
- conversational | |
- text-generation | |
language: | |
- pt | |
pretty_name: Portuguese Chat | |
license: cc-by-nc-4.0 | |
# Dataset Card for Portuguese Chat | |
We know that current English-first LLMs don’t work well for many other languages, both in terms of performance, latency, and speed. Building instruction datasets for non-English languages is an important challenge that needs to be solved. | |
Dedicated towards addressing this problem, I release 3 new datasets [rishiraj/portuguesechat](https://huggingface.co/datasets/rishiraj/portuguesechat/), [rishiraj/bengalichat](https://huggingface.co/datasets/rishiraj/bengalichat/) & [rishiraj/hindichat](https://huggingface.co/datasets/rishiraj/hindichat/) of 10,000 instructions and demonstrations each. This data can be used for supervised fine-tuning (SFT) to make language multilingual models follow instructions better. | |
### Dataset Summary | |
[rishiraj/portuguesechat](https://huggingface.co/datasets/rishiraj/portuguesechat/) was modelled after the instruction dataset described in OpenAI's [InstructGPT paper](https://huggingface.co/papers/2203.02155), and is translated from [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots/) which comprised mostly of single-turn instructions across the following categories: | |
| Category | Count | | |
|:-----------|--------:| | |
| Generation | 4560 | | |
| Open QA | 1240 | | |
| Brainstorm | 1120 | | |
| Chat | 850 | | |
| Rewrite | 660 | | |
| Summarize | 420 | | |
| Coding | 350 | | |
| Classify | 350 | | |
| Closed QA | 260 | | |
| Extract | 190 | | |
### Languages | |
The data in [rishiraj/portuguesechat](https://huggingface.co/datasets/rishiraj/portuguesechat/) are in Portuguese (BCP-47 pt). | |
### Data Fields | |
The data fields are as follows: | |
* `prompt`: Describes the task the model should perform. | |
* `prompt_id`: A unique ID for the prompt. | |
* `messages`: An array of messages, where each message indicates the role (system, user, assistant) and the content. | |
* `category`: Which category the example belongs to (e.g. `Chat` or `Coding`). | |
* `text`: Content of `messages` in a format that is compatible with dataset_text_field of SFTTrainer. | |
### Data Splits | |
| | train_sft | test_sft | | |
|---------------|------:| ---: | | |
| portuguesechat | 9500 | 500 | | |
### Licensing Information | |
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode). | |
### Citation Information | |
``` | |
@misc{portuguesechat, | |
author = {Rishiraj Acharya}, | |
title = {Portuguese Chat}, | |
year = {2023}, | |
publisher = {Hugging Face}, | |
journal = {Hugging Face repository}, | |
howpublished = {\url{https://huggingface.co/datasets/rishiraj/portuguesechat}} | |
} | |
``` |