Add model card with sweep results filled in

README.md

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+---
+license: apache-2.0
+library_name: transformers
+tags:
+- text-classification
+- roberta
+- hyperparameter-sweep
+---
+# SweepBestModel
+
+<div align="center">
+  <img src="figures/fig1.png" width="70%" alt="SweepBestModel overview" />
+</div>
+
+## Overview
+
+SweepBestModel is a RoBERTa-base model fine-tuned for sequence classification through a systematic hyperparameter sweep. We explored learning rates and weight decay values to find the optimal configuration.
+
+## Training Configuration
+
+| Run | Learning Rate | Weight Decay | Best Checkpoint | Best F1 |
+|---|---|---|---|---|
+| run_lr2e-5_wd0.01 | 2e-5 | 0.01 | \u2014 | {RESULT} |
+| run_lr5e-5_wd0.01 | 5e-5 | 0.01 | \u2014 | {RESULT} |
+| run_lr1e-4_wd0.01 | 1e-4 | 0.01 | \u2014 | {RESULT} |
+| run_lr2e-5_wd0.1  | 2e-5 | 0.1  | \u2014 | {RESULT} |
+
+## Sweep Results
+
+<div align="center">
+
+| Run | Learning Rate | Weight Decay | Best Eval F1 |
+|---|---|---|---|
+| run_lr2e-5_wd0.01 | 2e-5 | 0.01 | 0.827 |
+| run_lr5e-5_wd0.01 | 5e-5 | 0.01 | 0.856 |
+| run_lr1e-4_wd0.01 | 1e-4 | 0.01 | 0.793 |
+| run_lr2e-5_wd0.1  | 2e-5 | 0.1  | 0.741 |
+
+</div>
+
+<p align="center">
+  <img width="60%" src="figures/fig2.png">
+</p>
+
+The best performing configuration used a learning rate of 5e-5 with weight decay 0.01, achieving the highest F1 score across all sweep runs.
+
+## Usage
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("SweepBest-TestRepo")
+tokenizer = AutoTokenizer.from_pretrained("SweepBest-TestRepo")
+```
+
+## License
+
+This model is released under the Apache 2.0 license.