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README.md

@@ -1,33 +1,86 @@
 ---
-dataset_info:
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': neg
-          '1': pos
-  splits:
-  - name: train
-    num_bytes: 46258455.6
-    num_examples: 35000
-  - name: validation
-    num_bytes: 6608350.8
-    num_examples: 5000
-  - name: test
-    num_bytes: 13216701.6
-    num_examples: 10000
-  download_size: 42367081
-  dataset_size: 66083508.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+language:
+- en
+license: other
+pretty_name: IMDb Sentiment (35k/5k/10k)
+size_categories: 10K<n<100K
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+multilinguality:
+- monolingual
+source_datasets:
+- original
 ---
+
+# IMDb Sentiment Classification
+
+A curated version of the [Large Movie Review Dataset](https://ai.stanford.edu/~amaas/data/sentiment/) with custom train/validation/test splits optimized for model training and evaluation.
+
+## Dataset Summary
+
+This dataset contains **50,000 labeled movie reviews** from IMDb, each labeled as **positive (1)** or **negative (0)**. The data originates from the Stanford AI Lab's Large Movie Review Dataset, re-split into 35k/5k/10k for better validation during training.
+
+## Splits
+
+| Split | Samples | Positive | Negative |
+|-------|---------|----------|----------|
+| **train** | 35,000 | 17,500 | 17,500 |
+| **validation** | 5,000 | 2,500 | 2,500 |
+| **test** | 10,000 | 5,000 | 5,000 |
+| **Total** | **50,000** | **25,000** | **25,000** |
+
+The dataset is balanced — each split has roughly equal positive and negative reviews.
+
+## Data Fields
+
+- **`text`** (`string`): The movie review text (English).
+- **`label`** (`int`): Sentiment label — `0` for negative, `1` for positive.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("Mustafaege/imdb-sentiment")
+
+# Access splits
+train_ds = ds["train"]       # 35,000 samples
+val_ds = ds["validation"]    # 5,000 samples
+test_ds = ds["test"]         # 10,000 samples
+
+# Example
+print(train_ds[0])
+# {'text': 'This movie was absolutely fantastic...', 'label': 1}
+```
+
+## Source
+
+- **Original dataset**: [Stanford Large Movie Review Dataset](https://ai.stanford.edu/~amaas/data/sentiment/)
+- **Original HF mirror**: [stanfordnlp/imdb](https://huggingface.co/datasets/stanfordnlp/imdb)
+- **Paper**: Maas et al., "Learning Word Vectors for Sentiment Analysis", ACL 2011
+
+## Citation
+
+```bibtex
+@InProceedings{maas-EtAl:2011:ACL-HLT2011,
+  author    = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
+  title     = {Learning Word Vectors for Sentiment Analysis},
+  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
+  month     = {June},
+  year      = {2011},
+  address   = {Portland, Oregon, USA},
+  publisher = {Association for Computational Linguistics},
+  pages     = {142--150},
+  url       = {http://www.aclweb.org/anthology/P11-1015}
+}
+```
+
+## License
+
+The IMDb dataset is provided for academic research use. See the [original dataset page](https://ai.stanford.edu/~amaas/data/sentiment/) for licensing details.