Update README with new repositories (synthetic-data, deduplication, openai-oss)

- Add 3 new repositories to the script collections table
- Update featured scripts section with more diverse examples
- Include CPU-friendly deduplication example
- Add synthetic data generation with CoT reasoning example

README.md

@@ -40,6 +40,9 @@ hf jobs uv run --flavor l4x1 \
 | [classification](https://huggingface.co/datasets/uv-scripts/classification)     | Text classification with guaranteed valid outputs         | ✅           |
 | [dataset-creation](https://huggingface.co/datasets/uv-scripts/dataset-creation) | Create datasets from PDFs and files                       | ❌           |
 | [vllm](https://huggingface.co/datasets/uv-scripts/vllm)                         | High-performance inference with vLLM                      | ✅           |
+| [synthetic-data](https://huggingface.co/datasets/uv-scripts/synthetic-data)     | Generate high-quality synthetic data with CoT reasoning   | ✅           |
+| [deduplication](https://huggingface.co/datasets/uv-scripts/deduplication)       | Remove duplicates using semantic similarity               | ❌           |
+| [openai-oss](https://huggingface.co/datasets/uv-scripts/openai-oss)             | Generate responses with visible reasoning traces          | ✅           |
 
 ## 🎯 Why UV Scripts?
 
@@ -64,16 +67,27 @@ hf jobs uv run --flavor l4x1 \
   your-images extracted-text
 ```
 
-### Classify with Guaranteed Valid Outputs
+### Deduplicate Datasets (CPU-Friendly!)
 
-Text classification that always returns valid labels:
+Remove duplicates using semantic similarity - no GPU needed:
 
 ```bash
-# Uses vLLM's structured generation - no invalid outputs!
+# Fast semantic deduplication on CPU
+uv run https://huggingface.co/datasets/uv-scripts/deduplication/raw/main/semantic-dedupe.py \
+  your-dataset text your-dataset-clean \
+  --method duplicates --threshold 0.9
+```
+
+### Generate Synthetic Training Data
+
+Create high-quality synthetic data with chain-of-thought reasoning:
+
+```bash
+# Generate synthetic math problems with reasoning
 hf jobs uv run --flavor l4x1 \
-  https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset.py \
-  --input-dataset imdb --column text \
-  --labels "positive,negative" --output-dataset imdb-classified
+  https://huggingface.co/datasets/uv-scripts/synthetic-data/raw/main/cot-self-instruct.py \
+  --seed-dataset math-examples --output-dataset synthetic-math \
+  --task-type reasoning --num-samples 1000
 ```
 
 ## 🚀 Getting Started with HF Jobs