Update app.py

app.py

@@ -2,10 +2,12 @@ from datasets import load_dataset
 from evaluate import evaluator
 from transformers import AutoModelForSequenceClassification, pipeline, AutoTokenizer
 import gradio as gr
-data = load_dataset("huolongguo10/insecure",split="train").shuffle(seed=42).select(range(1000))
+data = load_dataset("huolongguo10/insecure",split="train").shuffle(seed=114514).select(range(1000))
 task_evaluator = evaluator("text-classification")
 model = AutoModelForSequenceClassification.from_pretrained("huolongguo10/check_sec")
 tokenizer = AutoTokenizer.from_pretrained("huolongguo10/check_sec")
+model_tiny = AutoModelForSequenceClassification.from_pretrained("huolongguo10/check_sec_tiny")
+tokenizer_tiny = AutoTokenizer.from_pretrained("huolongguo10/check_sec_tiny")
 # 1. Pass a model name or path
 eval_results = task_evaluator.compute(
     model_or_pipeline=model,
@@ -14,8 +16,17 @@ eval_results = task_evaluator.compute(
     label_mapping={"LABEL_0": 0, "LABEL_1": 1},
     tokenizer=tokenizer
 )
-
+eval_results_tiny = task_evaluator.compute(
+    model_or_pipeline=model_tiny,
+    data=data,
+    input_column="sentence1",
+    label_mapping={"LABEL_0": 0, "LABEL_1": 1},
+    tokenizer=tokenizer_tiny
+)
 with gr.Blocks() as demo:
+    gr.Markdown('# Base:')
     gr.JSON(eval_results)
+    gr.Markdown('# Tiny:')
+    gr.JSON(eval_results_tiny)
 print(eval_results)
 demo.launch()