gbert 0.5.3

Research-oriented multilingual manifesto analysis with comparative and corpus-level inference.

gbert

gbert is a research-oriented package for multilingual manifesto analysis. It provides a complete workflow for single-text inference, batch prediction, CMP lookup, corpus-level profiling, group comparison, country-year panel construction, UMAP projection, and publication-oriented plotting.

Installation

Install the base package:

pip install gbert

For UMAP and plotting, install the optional analysis stack:

pip install umap-learn matplotlib seaborn

Coverage Note

The package is designed for country-year inference with temporal coverage extending through 2023 in standard use. The built-in demo corpus remains a compact illustrative dataset for 1996-2018.

End-to-End Example

The following example mirrors the full workflow supported by the package.

1. Install the package

!pip -q install gbert

2. Import and initialize the model

Use GbertClassifier to load the runtime model and metadata. The model_repo_id argument points to the Hugging Face repository containing the model weights.

from gbert import GbertClassifier, load_demo_corpus_ja_us_de_1996_2018

model = GbertClassifier(
    model_repo_id="X-Li/gbert",
    # device="cpu",
)

3. Load the built-in demo corpus

load_demo_corpus_ja_us_de_1996_2018() returns an illustrative multilingual test corpus covering Japan, the United States, and Germany for each year from 1996 to 2018. The returned DataFrame includes text, country, year, and party_family.

demo = load_demo_corpus_ja_us_de_1996_2018()
texts = demo["text"].tolist()
countries = demo["country"].tolist()
years = demo["year"].tolist()
party_family = demo["party_family"].tolist()

4. Single prediction

Use predict() for one sentence at a time. The returned object contains ranked CMP predictions and macroeconomic covariates used during inference.

single = model.predict(
    "政府は先端産業への投資を強化する。",
    country="Japan",
    year=2026,
)
print(single["predictions"][:3])

5. Batch prediction

Use predict_batch() for multiple texts. Set return_df=True if you want a compact DataFrame instead of the full nested output.

batch = model.predict_batch(
    texts=texts,
    country=countries,
    year=years,
    return_df=True,
)
print(batch.head())

6. CMP code lookup

Use get_cmp_info() to inspect a specific CMP code and list_cmp_codes() to see the available label space.

print(model.get_cmp_info(305))
print(model.list_cmp_codes()[:10])

7. Corpus analysis

analyze_corpus() is the primary text-level analysis API. It returns a DataFrame with top predictions, entropy, confidence margin, and optional full posterior columns.

analysis = model.analyze_corpus(
    texts=texts,
    country=countries,
    year=years,
    include_probabilities=True,
)
print(analysis.columns[:20])
print(analysis[["country", "year", "top_cmp_code", "top_cmp_title", "top_probability", "entropy"]].head())

8. Topic profile

compute_topic_profile() aggregates sentence-level posterior probabilities into a corpus-level CMP profile.

profile = model.compute_topic_profile(
    texts=texts,
    country=countries,
    year=years,
)
print(profile.head(10))

9. Bootstrap topic profile

bootstrap_topic_profile() produces interval estimates for the corpus profile through repeated resampling.

bootstrap = model.bootstrap_topic_profile(
    texts=texts,
    country=countries,
    year=years,
    n_bootstrap=100,
)
print(bootstrap.head(10))

10. Group comparison

compare_groups() compares any user-defined grouping variable. Here the grouping variable is party_family.

comparison = model.compare_groups(
    texts=texts,
    country=countries,
    year=years,
    group=party_family,
)
print(comparison["group_summary"])
print(comparison["pairwise_divergence"].head())

11. Country profile comparison

compare_country_profiles() is a convenience wrapper for country-level comparison.

country_comparison = model.compare_country_profiles(
    texts=texts,
    country=countries,
    year=years,
)
print(country_comparison["group_summary"].head())

12. Country-year panel

panelize_country_year() converts text-level predictions into a country-year panel with topic scores and summary indicators.

panel = model.panelize_country_year(
    texts=texts,
    country=countries,
    year=years,
)
print(panel.head())

13. Methods-summary export

export_methods_summary() generates a compact dictionary for manuscript drafting, including sample statistics, prediction-quality summaries, top topics, and a ready-to-edit methods paragraph.

methods = model.export_methods_summary(
    texts=texts,
    country=countries,
    year=years,
)
print(methods.keys())
print(methods["methods_text"])

14. Raw UMAP projection

project_umap() returns the low-dimensional projection coordinates as a DataFrame.

umap_df = model.project_umap(
    texts=texts,
    country=countries,
    year=years,
)
print(umap_df.head())

15. UMAP plot

plot_umap() directly produces a seaborn-based figure and returns the figure, axes, and projected frame.

fig, ax, umap_frame = model.plot_umap(
    texts=texts,
    country=countries,
    year=years,
    color_by="country",
    annotate=False,
)

16. Topic profile bar plot

plot_topic_profile() visualizes the aggregated CMP profile.

fig, ax = model.plot_topic_profile(
    profile,
    top_n=12,
    title="Corpus Topic Profile",
)

17. Group divergence heatmap

plot_group_divergence() visualizes the pairwise Jensen-Shannon divergence returned by compare_groups().

fig, ax, divergence_matrix = model.plot_group_divergence(
    comparison["pairwise_divergence"],
    title="Party Family Divergence",
)

18. Topic heatmap from the country-year panel

plot_topic_heatmap() uses the panel output to visualize topic intensity over country-year cells or selected CMP codes.

fig, ax, heatmap = model.plot_topic_heatmap(
    panel,
    topics=[401, 504, 106],
    title="Selected Topic Heatmap",
)

19. Temporal trend plot

plot_temporal_trends() visualizes topic trajectories across years using the country-year panel.

fig, ax, trend_df = model.plot_temporal_trends(
    panel,
    countries=["Japan", "Germany", "France", "United Kingdom"],
    title="Temporal Topic Trends",
)

20. Ridgeplot from text-level analysis

plot_topic_ridgeplot() visualizes the distribution of posterior probabilities for selected CMP topics.

g = model.plot_topic_ridgeplot(
    analysis,
    topics=[504],
    title="Topic Probability Ridgeplot",
)

Main Interfaces

• predict(...)
• predict_batch(...)
• analyze_corpus(...)
• compute_topic_profile(...)
• bootstrap_topic_profile(...)
• compare_groups(...)
• compare_country_profiles(...)
• panelize_country_year(...)
• project_umap(...)
• plot_umap(...)
• plot_topic_profile(...)
• plot_group_divergence(...)
• plot_topic_heatmap(...)
• plot_topic_ridgeplot(...)
• plot_temporal_trends(...)
• export_methods_summary(...)
• get_cmp_info(...)
• list_cmp_codes(...)
• load_demo_corpus_ja_us_de_1996_2018()

The default text backbone is bert-base-multilingual-cased.

CMP Code Reference

The CMP labels exposed by get_cmp_info() and list_cmp_codes() follow the Comparative Manifesto Project coding scheme. For code definitions and the underlying coding framework, cite the Comparative Manifesto Project dataset and codebook in substantive applications.