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| import gradio as gr | |
| import json | |
| import pandas as pd | |
| import plotly.graph_objects as go | |
| # Load and process results | |
| with open("results.json") as f: | |
| results = json.load(f) | |
| def create_model_comparison_plot(results): | |
| # Extract all unique models | |
| models = set() | |
| for lang in results: | |
| for score in lang["scores"]: | |
| models.add(score["model"]) | |
| models = list(models) | |
| # Create traces for each model | |
| traces = [] | |
| for model in models: | |
| x_vals = [] # languages | |
| y_vals = [] # BLEU scores | |
| for lang in results: | |
| model_score = next( | |
| (s["bleu"] for s in lang["scores"] if s["model"] == model), None | |
| ) | |
| if model_score is not None: | |
| x_vals.append(lang["language_name"]) | |
| y_vals.append(model_score) | |
| traces.append( | |
| go.Bar( | |
| name=model.split("/")[-1], | |
| x=x_vals, | |
| y=y_vals, | |
| ) | |
| ) | |
| fig = go.Figure(data=traces) | |
| fig.update_layout( | |
| title="BLEU Scores by Model and Language", | |
| xaxis_title="Language", | |
| yaxis_title="BLEU Score", | |
| barmode="group", | |
| height=500, | |
| ) | |
| return fig | |
| def create_scatter_plot(results): | |
| fig = go.Figure() | |
| x_vals = [lang["speakers"] / 1_000_000 for lang in results] # 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="Language Coverage: Speakers vs BLEU Score", | |
| 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 fig | |
| def create_results_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"]) | |
| row = { | |
| "Language": 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": best_score["model"] if best_score["model"] is not None else "N/A", | |
| "Best Model BLEU": round(best_score["bleu"], 3) if best_score["bleu"] is not None else "N/A", | |
| } | |
| flat_data.append(row) | |
| return pd.DataFrame(flat_data) | |
| 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_name = score["model"].split("/")[-1] | |
| if model_name not in model_scores: | |
| model_scores[model_name] = { | |
| "High-Resource": [], | |
| "Mid-Resource": [], | |
| "Low-Resource": [] | |
| } | |
| model_scores[model_name][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(sum(categories["High-Resource"]) / len(categories["High-Resource"]), 3) if categories["High-Resource"] else 0 | |
| mid_avg = round(sum(categories["Mid-Resource"]) / len(categories["Mid-Resource"]), 3) if categories["Mid-Resource"] else 0 | |
| low_avg = round(sum(categories["Low-Resource"]) / len(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) | |
| leaderboard_data.append({ | |
| "Model": 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: "🥇" if x == 1 else "🥈" if x == 2 else "🥉" 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 df | |
| # 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" | |
| ) | |
| df = create_results_df(results) | |
| leaderboard_df = create_leaderboard_df(results) | |
| bar_plot = create_model_comparison_plot(results) | |
| scatter_plot = create_scatter_plot(results) | |
| gr.DataFrame(value=leaderboard_df, label="Model Leaderboard", show_search=False) | |
| gr.Plot(value=bar_plot, label="Model Comparison") | |
| gr.DataFrame(value=df, label="Language Results", show_search="search") | |
| gr.Plot(value=scatter_plot, label="Language Coverage") | |
| 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() | |