Update README.md

README.md

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----
-license: unknown
----
+---
+license: unknown
+task_categories:
+- text-classification
+language:
+- en
+size_categories:
+- 1M<n<10M
+---
+
+# About Dataset
+This dataset consists of a few million Amazon customer reviews (input text) and star ratings (output labels) for training fastText models for sentiment analysis.
+
+The dataset is based on real business data at a reasonable scale, which can be trained on a modest laptop in minutes.
+
+## Content
+The fastText supervised learning tutorial requires data in this format:
+
+`__label__<X> __label__<Y> ... <Text>`
+
+- `X` and `Y` are the class names, without quotes and all on one line.
+- In this dataset, the classes are `__label__1` and `__label__2`, with only one class per row.
+- `__label__1` corresponds to 1- and 2-star reviews, while `__label__2` corresponds to 4- and 5-star reviews.
+- 3-star reviews (neutral sentiment) are excluded from the dataset.
+- Review titles are prepended to the text, followed by a colon and a space.
+- Most reviews are in English, with a few in other languages like Spanish.
+
+## Source
+The data was obtained from Xiang Zhang's Google Drive directory in .csv format, which was then adapted for use with fastText.
+
+## Training and Testing
+Follow the instructions in the fastText supervised learning tutorial to set up the directory.
+
+### Training
+To train the model, use:
+
+```bash
+./fasttext supervised -input train.ft.txt -output model_amzn
+```
+
+## Acknowledgments
+
+Dataset obtained from https://www.kaggle.com/datasets/bittlingmayer/amazonreviews/data