Spaces:
Running
Running
File size: 2,262 Bytes
9631045 2ce0e9d 9631045 2ce0e9d 9631045 2ce0e9d 9631045 2ce0e9d 9631045 2ce0e9d 9631045 9b694d3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
import gradio as gr
import pandas as pd
import random
df = pd.read_csv('data.csv')
df_stats = pd.read_csv('data_stats_langs.csv')
map_models = df_stats[['model','model_name']].set_index('model').to_dict()
df = df.rename(columns=map_models['model_name'])
models = sorted(df.columns.tolist())
models.remove('hash')
models.remove('message')
messages = sorted(df['message'].tolist(), key=len)
messages_select = [(m[:250],m) for m in messages]
def out(message, model1, model2):
row = df[df['message'] == message]
output1 = row[model1].values[0]
output2 = row[model2].values[0]
return message, output1, output2
OUTPUT_DESCRIPTION='''How good are OpenSource LLMs in German? I've benchmarked a couple of models and generated outputs for about 250 prompts to compare the models.
For information about the used dataset and generation see the [README.md](https://huggingface.co/spaces/floleuerer/german_llm_outputs/blob/main/README.md)
Select a Prompt and the models you would like to compare -> hit "Show Outputs"
'''
BENCHMARK_DESCRIPTION='''# Columns
de: German Benchmark results (arc, hellaswag, mmlu)
en: English Benchmark results (arc, hellaswag, mmlu)
de_frac: Given a german prompt - how often does the model correctly respond in German?
'''
with gr.Blocks() as iface:
with gr.Tab('Model Outputs'):
gr.Markdown(OUTPUT_DESCRIPTION)
with gr.Row():
drop_message = gr.Dropdown(messages_select, label='Prompt', value=random.choice(messages))
with gr.Row():
drop_model1 = gr.Dropdown(models, label='Model 1', value=random.choice(models))
drop_model2 = gr.Dropdown(models, label='Model 2', value=random.choice(models))
with gr.Row():
btn = gr.Button("Show Outputs")
with gr.Row():
out_message = gr.TextArea(label='Prompt')
with gr.Row():
out_model1 = gr.TextArea(label='Output Model 1')
out_model2 = gr.TextArea(label='Output Model 2')
with gr.Tab('Benchmarks'):
gr.Markdown(BENCHMARK_DESCRIPTION)
gr.Dataframe(df_stats.drop('model', axis=1))
btn.click(out,
inputs=[drop_message, drop_model1, drop_model2],
outputs=[out_message, out_model1, out_model2])
iface.launch() |