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README.md

@@ -1,21 +1,50 @@
 ---
-dataset_info:
-  features:
-  - name: instruction
-    dtype: large_string
-  - name: input
-    dtype: large_string
-  - name: output
-    dtype: large_string
-  splits:
-  - name: train
-    num_bytes: 240465170
-    num_examples: 100000
-  download_size: 144905673
-  dataset_size: 240465170
+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-*
+    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"
+}
+```