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import json
import gradio as gr
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import pycountry
with open("results.json") as f:
results = json.load(f)
def mean(lst):
return sum(lst) / len(lst)
def create_leaderboard_df(results):
# Sort languages by average BLEU to determine resource categories
langs_with_bleu = [lang for lang in results if lang["bleu"] is not None]
sorted_langs = sorted(langs_with_bleu, key=lambda x: x["bleu"], reverse=True)
n_langs = len(sorted_langs)
high_cutoff = n_langs // 4 # top 25%
low_cutoff = n_langs - n_langs // 4 # bottom 25%
# Create sets of languages for each category
high_resource = {lang["language_name"] for lang in sorted_langs[:high_cutoff]}
low_resource = {lang["language_name"] for lang in sorted_langs[low_cutoff:]}
# Get all model scores with categorization
model_scores = {}
for lang in results:
category = (
"High-Resource"
if lang["language_name"] in high_resource
else "Low-Resource"
if lang["language_name"] in low_resource
else "Mid-Resource"
)
for score in lang["scores"]:
model = score["model"]
if model not in model_scores:
model_scores[model] = {
"High-Resource": [],
"Mid-Resource": [],
"Low-Resource": [],
}
model_scores[model][category].append(score["bleu"])
# Calculate average scores and create DataFrame
leaderboard_data = []
for model, categories in model_scores.items():
# Calculate averages for each category
high_avg = (
round(mean(categories["High-Resource"]), 3)
if categories["High-Resource"]
else 0
)
mid_avg = (
round(mean(categories["Mid-Resource"]), 3)
if categories["Mid-Resource"]
else 0
)
low_avg = (
round(mean(categories["Low-Resource"]), 3)
if categories["Low-Resource"]
else 0
)
# Calculate overall average
all_scores = (
categories["High-Resource"]
+ categories["Mid-Resource"]
+ categories["Low-Resource"]
)
overall_avg = round(sum(all_scores) / len(all_scores), 3)
model_name = model.split("/")[-1]
leaderboard_data.append(
{
"Model": f"[{model_name}](https://openrouter.ai/{model})",
"Overall BLEU": overall_avg,
"High-Resource BLEU": high_avg,
"Mid-Resource BLEU": mid_avg,
"Low-Resource BLEU": low_avg,
"Languages Tested": len(all_scores),
}
)
# Sort by overall BLEU
df = pd.DataFrame(leaderboard_data)
df = df.sort_values("Overall BLEU", ascending=False)
# Add rank and medals
df["Rank"] = range(1, len(df) + 1)
df["Rank"] = df["Rank"].apply(
lambda x: "π₯" if x == 1 else "π₯" if x == 2 else "π₯" if x == 3 else str(x)
)
# Reorder columns
df = df[
[
"Rank",
"Model",
"Overall BLEU",
"High-Resource BLEU",
"Mid-Resource BLEU",
"Low-Resource BLEU",
"Languages Tested",
]
]
return gr.DataFrame(
value=df,
label="Model Leaderboard",
show_search=False,
datatype=[
"number",
"markdown",
"number",
"number",
"number",
"number",
"number",
],
)
def create_model_comparison_plot(results):
top_languages = sorted(results, key=lambda x: x["speakers"], reverse=True)[:10]
scores_flat = [
{"language": lang["language_name"], "model": score["model"], "bleu": score["bleu"]}
for lang in top_languages
for score in lang["scores"]
]
df = pd.DataFrame(scores_flat)
fig = px.bar(df, x="language", y="bleu", color="model", barmode="group")
fig.update_layout(
title="BLEU Scores by Model and Language",
xaxis_title=None,
yaxis_title="BLEU Score",
barmode="group",
height=500,
legend=dict(
orientation="h", # horizontal orientation
yanchor="bottom",
y=-0.3, # position below plot
xanchor="center",
x=0.5, # center horizontally
),
)
return fig
def create_language_stats_df(results):
# Create a list to store flattened data
flat_data = []
for lang in results:
# Find the best model and its BLEU score
best_score = max(
lang["scores"] or [{"bleu": None, "model": None}], key=lambda x: x["bleu"]
)
model = best_score["model"]
model_name = model.split("/")[-1] if model else "N/A"
model_link = (
f"<a href='https://openrouter.ai/{model}' style='text-decoration: none; color: inherit;'>{model_name}</a>"
if model
else "N/A"
)
commonvoice_link = (
f"<!--{lang['commonvoice_hours']:07} (for sorting)--> <a href='https://commonvoice.mozilla.org/{lang['commonvoice_locale']}/speak' style='text-decoration: none; color: inherit;'>ποΈ {lang['commonvoice_hours']}</a>"
if lang["commonvoice_hours"]
else "N/A"
)
row = {
"Language": f"**{lang['language_name']}**",
"Speakers (M)": round(lang["speakers"] / 1_000_000, 1),
"Models Tested": len(lang["scores"]),
"Average BLEU": round(lang["bleu"], 3)
if lang["bleu"] is not None
else "N/A",
"Best Model": model_link,
"Best Model BLEU": round(best_score["bleu"], 3)
if best_score["bleu"] is not None
else "N/A",
"CommonVoice Hours": commonvoice_link,
}
flat_data.append(row)
df = pd.DataFrame(flat_data)
return gr.DataFrame(
value=df,
label="Language Results",
show_search="search",
datatype=[
"markdown",
"number",
"number",
"number",
"markdown",
"number",
"markdown",
],
)
def create_scatter_plot(results):
fig = go.Figure()
x_vals = [
lang["speakers"] / 1_000_000 for lang in results if lang["speakers"] >= 10_000
] # Convert to millions
y_vals = [lang["bleu"] for lang in results]
labels = [lang["language_name"] for lang in results]
fig.add_trace(
go.Scatter(
x=x_vals,
y=y_vals,
mode="markers+text",
text=labels,
textposition="top center",
hovertemplate="<b>%{text}</b><br>"
+ "Speakers: %{x:.1f}M<br>"
+ "BLEU Score: %{y:.3f}<extra></extra>",
)
)
fig.update_layout(
title=None,
xaxis_title="Number of Speakers (Millions)",
yaxis_title="Average BLEU Score",
height=500,
showlegend=False,
)
# Use log scale for x-axis since speaker numbers vary widely
fig.update_xaxes(type="log")
return gr.Plot(value=fig, label="Speaker population vs BLEU")
def format_number(n):
"""Format number with K/M suffix"""
if n >= 1_000_000:
return f"{n/1_000_000:.1f}M"
elif n >= 1_000:
return f"{n/1_000:.0f}K"
return str(n)
def create_world_map(results):
# Collect all country data
country_data = {}
for lang in results:
if "population" not in lang or lang["bleu"] is None:
continue
for country_code, speakers in lang["population"].items():
try:
# Convert alpha_2 (2-letter) to alpha_3 (3-letter) code
country = pycountry.countries.get(alpha_2=country_code)
if country is None:
continue
iso3_code = country.alpha_3
if iso3_code not in country_data:
country_data[iso3_code] = {
"total_speakers": 0,
"weighted_bleu_sum": 0,
"languages": [],
}
country_data[iso3_code]["total_speakers"] += speakers
country_data[iso3_code]["weighted_bleu_sum"] += speakers * lang["bleu"]
country_data[iso3_code]["languages"].append(
{
"name": lang["language_name"],
"speakers": speakers,
"bleu": lang["bleu"],
}
)
except (KeyError, AttributeError):
# Skip invalid or unrecognized country codes
continue
# Calculate final weighted averages and prepare hover text
countries = []
bleu_scores = []
hover_texts = []
def make_black_bar(value, max_width=10):
filled = int(value * max_width)
return "β¬οΈ" * filled + "β¬οΈ" * (max_width - filled)
def make_colored_bar(value, max_width=10):
"""Create a colored bar using Unicode blocks
π¦ for high values (>0.35)
π¨ for medium values (0.25-0.35)
π₯ for low values (<0.25)
β¬ for empty space
"""
filled = int(value * max_width)
filled = max(0, min(filled, max_width))
empty = max_width - filled
if value > 0.35:
return "π¦" * filled + "β¬" * empty
elif value > 0.25:
return "π¨" * filled + "β¬" * empty
else:
return "π₯" * filled + "β¬" * empty
for country_code, data in country_data.items():
weighted_avg = data["weighted_bleu_sum"] / data["total_speakers"]
try:
country_name = pycountry.countries.get(alpha_3=country_code).name
except AttributeError:
country_name = country_code
# Sort languages by number of speakers
langs = sorted(data["languages"], key=lambda x: x["speakers"], reverse=True)
total_speakers = sum(lang["speakers"] for lang in langs)
# Take top 5 languages and summarize the rest
main_langs = langs[:5]
other_langs = langs[5:]
# Create language rows with bars
lang_rows = []
for lang in main_langs:
percentage = (lang["speakers"] / total_speakers) * 100
speaker_bar = make_black_bar(percentage / 100)
bleu_bar = make_colored_bar((lang["bleu"] - 0.2) / 0.2)
lang_rows.append(
f"<b>{lang['name']}</b><br>"
f"{speaker_bar} {format_number(lang['speakers'])} speakers<br>"
f"{bleu_bar} {lang['bleu']:.3f} BLEU<br>"
)
# Add summary for other languages if any
if other_langs:
other_speakers = sum(lang["speakers"] for lang in other_langs)
other_percentage = (other_speakers / total_speakers) * 100
other_avg_bleu = sum(lang["bleu"] for lang in other_langs) / len(
other_langs
)
speaker_bar = make_black_bar(other_percentage / 100)
bleu_bar = make_colored_bar((other_avg_bleu - 0.2) / 0.2)
lang_rows.append(
f"<b>+{len(other_langs)} other languages</b><br>"
f"{speaker_bar} {format_number(other_speakers)} speakers<br>"
f"{bleu_bar} {other_avg_bleu:.3f} BLEU<br>"
)
# Create overall BLEU visualization
bleu_percentage = (weighted_avg - 0.2) / 0.2 # Scale from 0.2-0.4 to 0-1
overall_bleu_bar = make_colored_bar(bleu_percentage)
hover_text = (
f"<b>{country_name}</b><br><br>"
f"{format_number(data['total_speakers'])} speakers*<br>"
f"{overall_bleu_bar} {weighted_avg:.3f} BLEU<br><br>"
f"<b>Languages:</b><br><br>"
f"{'<br>'.join(lang_rows)}"
)
countries.append(country_code)
bleu_scores.append(weighted_avg)
hover_texts.append(hover_text)
# Create the choropleth map
fig = go.Figure(
data=go.Choropleth(
locations=countries,
locationmode="ISO-3",
z=bleu_scores,
text=hover_texts,
hoverinfo="text",
colorscale=[[0, "#ff9999"], [1, "#99ccff"]],
colorbar=dict(
title="BLEU Score",
orientation="h", # horizontal orientation
y=-0.2, # position below map
yanchor="bottom",
len=0.5, # length of colorbar
x=0.5, # center horizontally
xanchor="center",
thickness=20, # make it a bit thicker when horizontal
),
zmin=0.1,
zmax=0.5,
)
)
fig.update_layout(
title=dict(text="BLEU Score by Country", x=0.5, xanchor="center"),
geo=dict(
showframe=True,
showcoastlines=True,
projection_type="equal earth",
showland=True,
landcolor="#f8f9fa",
coastlinecolor="#e0e0e0",
countrycolor="#e0e0e0",
),
height=600,
margin=dict(l=0, r=0, t=30, b=0),
paper_bgcolor="white",
hoverlabel=dict(
bgcolor="beige",
font_size=12,
),
)
return fig
# Create the visualization components
with gr.Blocks(title="AI Language Translation Benchmark") as demo:
gr.Markdown("# AI Language Translation Benchmark")
gr.Markdown(
"Comparing translation performance across different AI models and languages"
)
bar_plot = create_model_comparison_plot(results)
world_map = create_world_map(results)
create_leaderboard_df(results)
gr.Plot(value=bar_plot, label="Model Comparison")
create_language_stats_df(results)
create_scatter_plot(results)
gr.Plot(value=world_map, container=False, elem_classes="fullwidth-plot")
gr.Markdown(
"""
## Methodology
### Dataset
- Using [FLORES-200](https://huggingface.co/datasets/openlanguagedata/flores_plus) evaluation set, a high-quality human-translated benchmark comprising 200 languages
- Each language is tested with the same 100 sentences
- All translations are from the evaluated language to a fixed set of representative languages sampled by number of speakers
- Language statistics sourced from Ethnologue and Wikidata
### Models & Evaluation
- Models accessed through [OpenRouter](https://openrouter.ai/), including fast models of all big labs, open and closed
- **BLEU Score**: Translations are evaluated using the BLEU metric, which measures how similar the AI's translation is to a human reference translation -- higher is better
### Language Categories
Languages are divided into three tiers based on translation difficulty:
- High-Resource: Top 25% of languages by BLEU score (easiest to translate)
- Mid-Resource: Middle 50% of languages
- Low-Resource: Bottom 25% of languages (hardest to translate)
""",
container=True,
)
demo.launch()
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