Datasets:
| language: | |
| - en | |
| license: mit | |
| task_categories: | |
| - text-classification | |
| tags: | |
| - sentiment | |
| - alpaca | |
| - sft | |
| - instruction-tuning | |
| size_categories: | |
| - 100K<n<1M | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-*.parquet | |
| # summarization-sft-100k | |
| Sentiment classification dataset in Alpaca instruction format for supervised fine-tuning (SFT). | |
| **100,000 examples** sourced and normalised from: | |
| - [mteb/mteb-human-tweet-sentiment-classification](https://huggingface.co/datasets/mteb/mteb-human-tweet-sentiment-classification) | |
| - [stanfordnlp/sst2](https://huggingface.co/datasets/stanfordnlp/sst2) | |
| - [SetFit/sst5](https://huggingface.co/datasets/SetFit/sst5) (5-class collapsed to 3) | |
| - [jbeno/sentiment_merged](https://huggingface.co/datasets/jbeno/sentiment_merged) | |
| ## Label space | |
| All labels normalised to 3 classes: `negative`, `neutral`, `positive` | |
| ## Format | |
| | Column | Description | | |
| |--------|-------------| | |
| | `instruction` | Task prompt with label options | | |
| | `input` | The text to classify | | |
| | `output` | One of: negative, neutral, positive | | |
| ## Example | |
| ```json | |
| { | |
| "instruction": "Classify the sentiment of the following text. Respond with one of: negative, neutral, positive.", | |
| "input": "This was an absolutely wonderful experience.", | |
| "output": "positive" | |
| } | |
| ``` | |