Update app.py
Browse files
app.py
CHANGED
@@ -23,7 +23,8 @@ model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float
|
|
23 |
#μ§μ΄ μ΅μ
: device_map="auto"
|
24 |
|
25 |
# KMMLU λ°μ΄ν°μ
λ‘λ
|
26 |
-
dataset = load_dataset("HAERAE-HUB/KMMLU", "Accounting")
|
|
|
27 |
df = dataset['test'].to_pandas()
|
28 |
|
29 |
def evaluate_model(question, choices):
|
@@ -62,10 +63,12 @@ def run_kmmlu_test(subject):
|
|
62 |
summary = f"μ£Όμ : {subject}\nμ νλ: {accuracy:.2%} ({correct}/{total})\n\n"
|
63 |
return summary + "\n".join(results)
|
64 |
|
|
|
|
|
65 |
iface = gr.Interface(
|
66 |
fn=run_kmmlu_test,
|
67 |
inputs="Accounting",
|
68 |
-
|
69 |
outputs="text",
|
70 |
title="Llama 3λ₯Ό μ΄μ©ν KMMLU ν
μ€νΈ",
|
71 |
description="μ νν μ£Όμ μ λν΄ KMMLU ν
μ€νΈλ₯Ό μ€νν©λλ€."
|
|
|
23 |
#μ§μ΄ μ΅μ
: device_map="auto"
|
24 |
|
25 |
# KMMLU λ°μ΄ν°μ
λ‘λ
|
26 |
+
#dataset = load_dataset("HAERAE-HUB/KMMLU", "Accounting")
|
27 |
+
dataset = load_dataset("HAERAE-HUB/KMMLU")
|
28 |
df = dataset['test'].to_pandas()
|
29 |
|
30 |
def evaluate_model(question, choices):
|
|
|
63 |
summary = f"μ£Όμ : {subject}\nμ νλ: {accuracy:.2%} ({correct}/{total})\n\n"
|
64 |
return summary + "\n".join(results)
|
65 |
|
66 |
+
subjects=df['subject'].unique().tolist()
|
67 |
+
|
68 |
iface = gr.Interface(
|
69 |
fn=run_kmmlu_test,
|
70 |
inputs="Accounting",
|
71 |
+
inputs=gr.Dropdown(choices=subjects, label="μ£Όμ μ ν"),
|
72 |
outputs="text",
|
73 |
title="Llama 3λ₯Ό μ΄μ©ν KMMLU ν
μ€νΈ",
|
74 |
description="μ νν μ£Όμ μ λν΄ KMMLU ν
μ€νΈλ₯Ό μ€νν©λλ€."
|