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README.md

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+---
+license: mit
+task_categories:
+  - text-classification
+language:
+  - en
+size_categories:
+  - n<1K
+---
+
+# BestRunClassifier Dataset
+
+This dataset contains the training and validation splits from the best-performing hyperparameter run of our text classifier.
+
+## Run Configuration
+
+| Parameter | Value |
+|---|---|
+| Learning Rate | 0.0003 |
+| Batch Size | 32 |
+| Epochs | 20 |
+| Dropout | 0.3 |
+
+## Metrics
+
+| Metric | Value |
+|---|---|
+| Train Loss | 0.195 |
+| Val Loss | 0.342 |
+| Train Accuracy | 0.941 |
+| Val Accuracy | 0.872 |
+| Train F1 | 0.938 |
+| Val F1 | 0.847 |
+
+## Dataset Structure
+
+- `train.csv`: Training split with columns `text` and `label` (positive/negative/neutral).
+- `val.csv`: Validation split with columns `text` and `label` (positive/negative/neutral).
+- `metrics.json`: Full metrics and configuration for the best run.
+
+## Usage
+
+```python
+from datasets import load_dataset
+ds = load_dataset("username/BestRunClassifier-Data")
+```
+
+## License
+
+This dataset is released under the MIT License.

metrics.json

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+{
+  "run_id": "run_003",
+  "config": {
+    "learning_rate": 0.0003,
+    "batch_size": 32,
+    "epochs": 20,
+    "dropout": 0.3
+  },
+  "metrics": {
+    "train_loss": 0.195,
+    "val_loss": 0.342,
+    "train_accuracy": 0.941,
+    "val_accuracy": 0.872,
+    "train_f1": 0.938,
+    "val_f1": 0.847
+  }
+}

train.csv

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+text,label
+"Superb craftsmanship and design",positive
+"Failed to meet advertised claims",negative
+"Reasonable quality for budget option",neutral
+"Best in class, truly exceptional",positive
+"Arrived late and missing parts",negative
+"Fair product with minor issues",neutral
+"Top-notch, will definitely buy again",positive
+"Horrible, returned immediately",negative
+"Acceptable but room for improvement",neutral

val.csv

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+text,label
+"Absolutely wonderful, five stars",positive
+"Defective unit, requesting refund",negative
+"Does what it says, no more no less",neutral