Update app.py

app.py

@@ -34,25 +34,25 @@ def benchmark(model_name, dataset_name):
     sequence_lengths = [len(sample["input_ids"]) for sample in tokenized_dataset]
     total_tokens = sum(sequence_lengths)
 
-    print("Computing the truncation ratios")
-    truncation_ratios = []
+    print("Computing the truncation percentages")
+    truncation_percentages = []
     recommended = None
     for max_len in TRUNCATION_LENGTHS:
         total_truncated_tokens = sum(max(length - max_len, 0) for length in sequence_lengths)
-        truncation_ratio = total_truncated_tokens / total_tokens * 100
-        truncation_ratios.append(truncation_ratio)
-        if recommended is None and truncation_ratio < 5.0:
+        truncation_percentage = total_truncated_tokens / total_tokens * 100
+        truncation_percentages.append(truncation_percentage)
+        if recommended is None and truncation_percentage < 5.0:
             recommended = max_len
 
     hist = np.histogram(sequence_lengths, bins=50)
     lengths_distribution = pd.DataFrame({
         "max_length": (hist[1][:-1] + hist[1][1:])/2,
-        "Ratio (%)": hist[0]/N_SAMPLES*100,
+        "Percentage (%)": hist[0]/N_SAMPLES*100,
     })
 
     truncation_data = pd.DataFrame({
         "max_length": [str(value) for value in TRUNCATION_LENGTHS],
-        "Ratio (%)": truncation_ratios,
+        "Percentage (%)": truncation_percentages,
     })
 
     return lengths_distribution, truncation_data, CODE_TEMPLATE.format(recommended)
@@ -61,12 +61,12 @@ with gr.Blocks() as demo:
     model_input = gr.Textbox(label="Model Name", value="Qwen/Qwen3-0.6B")
     dataset_input = gr.Textbox(label="Dataset Name", value="trl-lib/tldr")
     run_button = gr.Button("Run estimation")
-    lengths_plot = gr.BarPlot(None, title="Length distribution", x="max_length", y="Ratio (%)")
-    truncation_ratio_plot = gr.BarPlot(None, title="Truncation ratio (how many tokens are discarded)", x="max_length", y="Ratio (%)")
+    lengths_plot = gr.BarPlot(None, title="Length distribution", x="max_length", y="Percentage (%)")
+    truncation_percentage_plot = gr.BarPlot(None, title="Truncation percentage (how many tokens are discarded)", x="max_length", y="Percentage (%)")
 
     recommended_code = gr.Code(CODE_TEMPLATE.format("..."), language="python", label="Recommended configuration")
 
-    run_button.click(fn=benchmark, inputs=[model_input, dataset_input], outputs=[lengths_plot, truncation_ratio_plot, recommended_code])
+    run_button.click(fn=benchmark, inputs=[model_input, dataset_input], outputs=[lengths_plot, truncation_percentage_plot, recommended_code])
 
     with gr.Accordion("See details", open=False):
         gr.Markdown("""
@@ -77,7 +77,7 @@ This tool helps you choose an appropriate `max_length` value for your SFT traini
 - Prepares and tokenizes the data exactly as `SFTTrainer` would.
 - Generates two visualizations:
     - **Sequence Length Distribution:** Shows how long your tokenized sequences are.
-    - **Truncation Ratio:** Estimates the percentage of tokens that would be discarded (truncated) for different `max_length` values.
+    - **Truncation Percentage:** Estimates the percentage of tokens that would be discarded (truncated) for different `max_length` values.
 - Recommends the smallest `max_length` where truncation affects less than 5% of the tokens.
 
 Use this tool to balance efficiency and memory usage when setting your `max_length` parameter.