Update script usage examples to use filename + `classify` command.

README.md

@@ -22,7 +22,7 @@ This is a modified version of https://huggingface.co/datasets/uv-scripts/classif
 
 ```bash
 # Classify IMDB reviews
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset stanfordnlp/imdb \
   --column text \
   --labels "positive,negative" \
@@ -63,7 +63,7 @@ That's it! No installation, no setup - just `uv run`.
 ### Basic Classification
 
 ```bash
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset <dataset-id> \
   --column <text-column> \
   --labels <comma-separated-labels> \
@@ -98,7 +98,7 @@ uv run examples/classify-dataset.py \
 Provide context for your labels to improve classification accuracy:
 
 ```bash
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset user/support-tickets \
   --column content \
   --labels "bug,feature,question,other" \
@@ -114,7 +114,7 @@ The model uses these descriptions to better understand what each label represent
 Enable multi-label mode for documents that can have multiple applicable labels:
 
 ```bash
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset ag_news \
   --column text \
   --labels "world,sports,business,science" \
@@ -128,7 +128,7 @@ uv run examples/classify-dataset.py \
 ### Sentiment Analysis
 
 ```bash
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset stanfordnlp/imdb \
   --column text \
   --labels "positive,ambivalent,negative" \
@@ -139,7 +139,7 @@ uv run examples/classify-dataset.py \
 ### Support Ticket Classification
 
 ```bash
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset user/support-tickets \
   --column content \
   --labels "bug,feature_request,question,other" \
@@ -151,7 +151,7 @@ uv run examples/classify-dataset.py \
 ### News Categorization
 
 ```bash
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset ag_news \
   --column text \
   --labels "world,sports,business,tech" \
@@ -162,7 +162,7 @@ uv run examples/classify-dataset.py \
 ### Multi-Label News Classification
 
 ```bash
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset ag_news \
   --column text \
   --labels "world,sports,business,tech" \
@@ -180,7 +180,7 @@ Classify academic papers into machine learning research areas:
 
 ```bash
 # Fast classification with random sampling
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset librarian-bots/arxiv-metadata-snapshot \
   --column abstract \
   --labels "llm,computer_vision,reinforcement_learning,optimization,theory,other" \
@@ -192,7 +192,7 @@ uv run examples/classify-dataset.py \
   --shuffle
 
 # Multi-label for nuanced classification
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset librarian-bots/arxiv-metadata-snapshot \
   --column abstract \
   --labels "multimodal,agents,reasoning,safety,efficiency" \
@@ -212,7 +212,7 @@ This script is optimized to run locally on GPU-equipped machines:
 
 ```bash
 # Local execution with your GPU
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset stanfordnlp/imdb \
   --column text \
   --labels "positive,negative" \
@@ -230,7 +230,7 @@ When working with ordered datasets, use `--shuffle` with `--max-samples` to get
 
 ```bash
 # Get 50 random reviews instead of the first 50
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset stanfordnlp/imdb \
   --column text \
   --labels "positive,negative" \
@@ -248,7 +248,7 @@ By default, this script works with any instruction-tuned model. Here are some re
 
 ```bash
 # Lightweight model for fast classification
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset user/my-dataset \
   --column text \
   --labels "A,B,C" \
@@ -256,7 +256,7 @@ uv run examples/classify-dataset.py \
   --output-dataset user/classified
 
 # Larger model for complex classification
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset user/legal-docs \
   --column text \
   --labels "contract,patent,brief,memo,other" \
@@ -264,7 +264,7 @@ uv run examples/classify-dataset.py \
   --output-dataset user/legal-classified
 
 # Specialized zero-shot classifier
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset user/my-dataset \
   --column text \
   --labels "A,B,C" \
@@ -277,7 +277,7 @@ uv run examples/classify-dataset.py \
 Configure `--batch-size` for more effective batch processing with large datasets: 
 
 ```bash
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset user/huge-dataset \
   --column text \
   --labels "A,B,C" \
@@ -342,7 +342,7 @@ Start with small tests, then run on the full dataset:
 
 ```bash
 # Step 1: Test with small sample
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset your-dataset \
   --column text \
   --labels "label1,label2,label3" \
@@ -351,7 +351,7 @@ uv run examples/classify-dataset.py \
   --max-samples 100
 
 # Step 2: If results look good, run on full dataset
-uv run examples/classify-dataset.py \
+uv run classify-dataset.py classify \
   --input-dataset your-dataset \
   --column text \
   --labels "label1,label2,label3" \