Update README.md to enhance model description and add advanced example for ArXiv ML trends analysis

README.md

@@ -32,6 +32,7 @@ That's it! No installation, no setup - just `uv run`.
 - **Guaranteed valid outputs** using vLLM's guided decoding with outlines
 - **Zero-shot classification** with structured generation
 - **GPU-optimized** with vLLM's automatic batching for maximum efficiency
+- **Default model**: HuggingFaceTB/SmolLM3-3B (fast 3B model, easily changeable)
 - **Robust text handling** with preprocessing and validation
 - **Three prompt styles** for different use cases
 - **Automatic progress tracking** and detailed statistics
@@ -52,13 +53,15 @@ uv run classify-dataset.py \
 ### Arguments
 
 **Required:**
+
 - `--input-dataset`: Hugging Face dataset ID (e.g., `stanfordnlp/imdb`, `user/my-dataset`)
 - `--column`: Name of the text column to classify
 - `--labels`: Comma-separated classification labels (e.g., `"spam,ham"`)
 - `--output-dataset`: Where to save the classified dataset
 
 **Optional:**
-- `--model`: Model to use (default: `HuggingFaceTB/SmolLM3-3B`)
+
+- `--model`: Model to use (default: **`HuggingFaceTB/SmolLM3-3B`** - a fast 3B parameter model)
 - `--prompt-style`: Choose from `simple`, `detailed`, or `reasoning` (default: `simple`)
 - `--split`: Dataset split to process (default: `train`)
 - `--max-samples`: Limit samples for testing
@@ -77,6 +80,7 @@ All styles benefit from structured output guarantees - the model can only output
 ## 📊 Examples
 
 ### Sentiment Analysis
+
 ```bash
 uv run classify-dataset.py \
   --input-dataset stanfordnlp/imdb \
@@ -86,6 +90,7 @@ uv run classify-dataset.py \
 ```
 
 ### Support Ticket Classification
+
 ```bash
 uv run classify-dataset.py \
   --input-dataset user/support-tickets \
@@ -96,6 +101,7 @@ uv run classify-dataset.py \
 ```
 
 ### News Categorization
+
 ```bash
 uv run classify-dataset.py \
   --input-dataset ag_news \
@@ -109,30 +115,17 @@ uv run classify-dataset.py \
 
 This script is optimized for [Hugging Face Jobs](https://huggingface.co/docs/hub/spaces-gpu-jobs) (requires Pro subscription or Team/Enterprise organization):
 
-```bash
+````bash
 # Run on L4 GPU with vLLM image
 hf jobs uv run \
   --flavor l4x1 \
   --image vllm/vllm-openai:latest \
-  classify-dataset.py \
+  https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset.py \
   --input-dataset stanfordnlp/imdb \
   --column text \
   --labels "positive,negative" \
   --output-dataset user/imdb-classified
 
-# Run on A10 GPU with custom model
-hf jobs uv run \
-  --flavor a10g-large \
-  --image vllm/vllm-openai:latest \
-  classify-dataset.py \
-  --input-dataset user/reviews \
-  --column review_text \
-  --labels "1,2,3,4,5" \
-  --output-dataset user/reviews-rated \
-  --model mistralai/Mistral-7B-Instruct-v0.3 \
-  --prompt-style detailed
-```
-
 ### GPU Flavors
 - `t4-small`: Budget option for smaller models
 - `l4x1`: Good balance for 7B models
@@ -144,7 +137,7 @@ hf jobs uv run \
 
 ### Using Different Models
 
-The default model is SmolLM3-3B, but you can use any instruction-tuned model:
+By default, this script uses **HuggingFaceTB/SmolLM3-3B** - a fast, efficient 3B parameter model that's perfect for most classification tasks. You can easily use any other instruction-tuned model:
 
 ```bash
 # Larger model for complex classification
@@ -154,7 +147,7 @@ uv run classify-dataset.py \
   --labels "contract,patent,brief,memo,other" \
   --output-dataset user/legal-classified \
   --model Qwen/Qwen2.5-7B-Instruct
-```
+````
 
 ### Large Datasets
 
@@ -170,7 +163,7 @@ uv run classify-dataset.py \
 
 ## 📈 Performance
 
-- **SmolLM3-3B**: ~50-100 texts/second on A10
+- **SmolLM3-3B (default)**: ~50-100 texts/second on A10
 - **7B models**: ~20-50 texts/second on A10
 - vLLM automatically optimizes batching for best throughput
 
@@ -186,32 +179,76 @@ The script loads your dataset, preprocesses texts, classifies each one using gui
 ## 🐛 Troubleshooting
 
 ### CUDA Not Available
+
 This script requires a GPU. Run it on:
+
 - A machine with NVIDIA GPU
 - HF Jobs (recommended)
 - Cloud GPU instances
 
 ### Out of Memory
+
 - Use a smaller model
 - Use a larger GPU (e.g., a100-large)
 
 ### Invalid/Skipped Texts
+
 - Texts shorter than 3 characters are skipped
 - Empty or None values are marked as invalid
 - Very long texts are truncated to 4000 characters
 
 ### Classification Quality
+
 - With guided decoding, outputs are guaranteed to be valid labels
 - For better results, use clear and distinct label names
 - Try the `reasoning` prompt style for complex classifications
 - Use a larger model for nuanced tasks
 
 ### vLLM Version Issues
+
 If you see `ImportError: cannot import name 'GuidedDecodingParams'`:
+
 - Your vLLM version is too old (requires >= 0.6.6)
 - The script specifies the correct version in its dependencies
 - UV should automatically install the correct version
 
+## 🔬 Advanced Example: ArXiv ML Trends Analysis
+
+For a more complex real-world example, we provide scripts to analyze ML research trends from ArXiv papers:
+
+### Step 1: Prepare the Dataset
+
+```bash
+# Filter and prepare ArXiv CS papers from 2024
+uv run prepare_arxiv_2024.py
+```
+
+This creates a filtered dataset of CS papers with combined title+abstract text.
+
+### Step 2: Run Classification with Python API
+
+```bash
+# Use HF Jobs Python API to classify papers
+uv run run_arxiv_classification.py
+```
+
+This script demonstrates:
+
+- Using `run_uv_job()` from the Python API
+- Classifying into modern ML trends (reasoning, agents, multimodal, robotics, etc.)
+- Handling authentication and job monitoring
+
+The classification categories include:
+
+- `reasoning_systems`: Chain-of-thought, reasoning, problem solving
+- `agents_autonomous`: Agents, tool use, autonomous systems
+- `multimodal_models`: Vision-language, audio, multi-modal
+- `robotics_embodied`: Robotics, embodied AI, manipulation
+- `efficient_inference`: Quantization, distillation, edge deployment
+- `alignment_safety`: RLHF, alignment, safety, interpretability
+- `generative_models`: Diffusion, generation, synthesis
+- `foundational_other`: Other foundational ML/AI research
+
 ## 📝 License
 
-This script is provided as-is for use with the UV Scripts organization.
+This script is provided as-is for use with the UV Scripts organization.