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---
license: mit
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 1906560
num_examples: 9543
- name: validation
num_bytes: 479540
num_examples: 2388
download_size: 728648
dataset_size: 2386100
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
[zeroshot/twitter-financial-news-sentiment](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment) prepared for LLM fine-tuning
by adding an `instruction` column and mapping the label from numeric to string (`{0:"negative", 1:'positive', 2:'neutral'}`).
[Source](https://github.com/AI4Finance-Foundation/FinGPT/blob/master/fingpt/FinGPT-v3/data/making_data.ipynb)
```python
from datasets import load_dataset
import datasets
from huggingface_hub import notebook_login
notebook_login()
ds = load_dataset('zeroshot/twitter-financial-news-sentiment')
num_to_label = {
0: 'negative',
1: 'positive',
2: 'neutral',
}
instruction = 'What is the sentiment of this tweet? Please choose an answer from {negative/neutral/positive}.'
# Training split
ds_train = ds['train']
ds_train = ds_train.to_pandas()
ds_train['label'] = ds_train['label'].apply(num_to_label.get)
ds_train['instruction'] = instruction
ds_train.columns = ['input', 'output', 'instruction']
ds_train = datasets.Dataset.from_pandas(ds_train)
ds_train.push_to_hub("twitter-financial-news-sentiment")
# Validation split
ds_valid = ds['validation']
ds_valid = ds_valid.to_pandas()
ds_valid['label'] = ds_valid['label'].apply(num_to_label.get)
ds_valid['instruction'] = instruction
ds_valid.columns = ['input', 'output', 'instruction']
ds_valid = datasets.Dataset.from_pandas(ds_valid, split='validation')
ds_valid.push_to_hub("twitter-financial-news-sentiment", split='validation')
```
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