Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
Thai
Size:
10K - 100K
Tags:
instruct-fellow
License:
| language: | |
| - th | |
| license: cc0-1.0 | |
| size_categories: | |
| - 10K<n<100K | |
| task_categories: | |
| - text-generation | |
| - text2text-generation | |
| pretty_name: i | |
| dataset_info: | |
| features: | |
| - name: inputs | |
| dtype: string | |
| - name: targets | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 10132750 | |
| num_examples: 16194 | |
| - name: validation | |
| num_bytes: 1118295 | |
| num_examples: 1777 | |
| - name: test | |
| num_bytes: 1240521 | |
| num_examples: 1965 | |
| download_size: 3093175 | |
| dataset_size: 12491566 | |
| tags: | |
| - instruct-fellow | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| - split: validation | |
| path: data/validation-* | |
| - split: test | |
| path: data/test-* | |
| wisesight_sentiment_prompt is the instruct fellow dataset for sentiment Thai text by prompt. It can use fine-tuning model. | |
| - inputs: Prompt | |
| - targets: Text targets that AI should answer. | |
| **Template** | |
| ``` | |
| Inputs: จำแนกประโยคต่อไปนี้เป็นคำถามหรือข้อความเชิงบวก/เป็นกลาง/เชิงลบ:\n{text} | |
| targets: ประโยคที่กำหนดสามารถจำแนกข้อความได้เป็นข้อความ{category} | |
| ``` | |
| category | |
| - คำถาม: question | |
| - เชิงบวก: positive | |
| - เป็นกลาง: neutral | |
| - เชิงลบ: negative | |
| Notebook that used create this dataset: [https://github.com/PyThaiNLP/support-aya-datasets/blob/main/sentiment-analysis/wisesight_sentiment.ipynb](https://github.com/PyThaiNLP/support-aya-datasets/blob/main/sentiment-analysis/wisesight_sentiment.ipynb) | |
| Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question) | |
| * Released to public domain under Creative Commons Zero v1.0 Universal license. | |
| * Size: 26,737 messages | |
| * Language: Central Thai | |
| * Style: Informal and conversational. With some news headlines and advertisement. | |
| * Time period: Around 2016 to early 2019. With small amount from other period. | |
| * Domains: Mixed. Majority are consumer products and services (restaurants, cosmetics, drinks, car, hotels), with some current affairs. | |
| See more: [wisesight_sentiment](https://huggingface.co/datasets/wisesight_sentiment). | |
| PyThaiNLP |