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bertopic-conflictos-chile-v22-complete

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train · 1 rows

The dataset viewer is not available for this split.

Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
version: string
created: string
k_natural: int64
total_docs: int64
outliers_final: int64
config: struct<dedup_threshold: double, min_keywords_safe: int64, mmr_diversity: double, umap_n_neighbors: int64, hdbscan_min_cluster_size: int64>
metrics: struct<topic_diversity: double, inverted_rbo: double, silhouette_score: double, calinski_harabasz: double, davies_bouldin: double, outlier_ratio: double, mean_size: double, std_size: double, min_size: int64, max_size: int64, gini_coefficient: double, n_topics: int64, mean_distance: double, min_distance: double, max_distance: double, std_distance: double, we_centroid_distance: double, we_pd_mean: double, we_pd_min: double, we_pd_max: double, we_pd_std: double, pairwise_jaccard_distance: double, mean_umass_coherence: double, mean_npmi_coherence: double, mean_cv_coherence: double>
features: struct<model_saved: bool, all_visualizations: bool, hierarchical_topics: bool, temporal_analysis: bool, outlier_reduction: bool, mathematical_metrics: bool>
vs
global: struct<topic_diversity: double, inverted_rbo: double, silhouette_score: double, calinski_harabasz: double, davies_bouldin: double, outlier_ratio: double, mean_size: double, std_size: double, min_size: int64, max_size: int64, gini_coefficient: double, n_topics: int64, mean_distance: double, min_distance: double, max_distance: double, std_distance: double, we_centroid_distance: double, we_pd_mean: double, we_pd_min: double, we_pd_max: double, we_pd_std: double, pairwise_jaccard_distance: double, mean_umass_coherence: double, mean_npmi_coherence: double, mean_cv_coherence: double>
per_topic_coherence: struct<0: struct<umass: double, npmi: double>, 1: struct<umass: double, npmi: double>, 2: struct<umass: double, npmi: double>, 3: struct<umass: double, npmi: double>, 4: struct<umass: double, npmi: double>, 5: struct<umass: double, npmi: double>, 6: struct<umass: double, npmi: double>, 7: struct<umass: double, npmi: double>, 8: struct<umass: double, npmi: double>, 9: struct<umass: double, npmi: double>, 10: struct<umass: double, npmi: double>, 11: struct<umass: double, npmi: double>, 12: struct<umass: double, npmi: double>, 13: struct<umass: double, npmi: double>, 14: struct<umass: double, npmi: double>, 15: struct<umass: double, npmi: double>, 16: struct<umass: double, npmi: double>, 17: struct<umass: double, npmi: double>, 18: struct<umass: double, npmi: double>, 19: struct<umass: double, npmi: double>, 20: struct<umass: double, npmi: double>, 21: struct<umass: double, npmi: double>, 22: struct<umass: double, npmi: double>, 23: struct<umass: double, npmi: double>, 24: struct<umass: double, npmi: double>, 25: struct<umass: double, npmi: double>, 26: struct<umass: double, npmi: double>, 27: struct<umass: double, npmi: double>, 28: struct<umass: double, npmi: double>, 29: struct<umass: double, npmi: double>, 30: struct<umass: double, npmi: double>, 31: struct<umass: double, npmi: double>, 32: struct<umass: double, npmi: double>, 33: struct<umass: double, npmi: double>, 34: struct<umass: double, npmi: double>, 35: struct<umass: double, npmi: double>, 36: struct<umass: double, npmi: double>, 37: struct<umass: double, npmi: double>, 38: struct<umass: double, npmi: double>, 39: struct<umass: double, npmi: double>, 40: struct<umass: double, npmi: double>, 41: struct<umass: double, npmi: double>, 42: struct<umass: double, npmi: double>, 43: struct<umass: double, npmi: double>, 44: struct<umass: double, npmi: double>, 45: struct<umass: double, npmi: double>, 46: struct<umass: double, npmi: double>, 47: struct<umass: double, npmi: double>, 48: struct<umass: double, npmi: double>, 49: struct<umass: double, npmi: double>, 50: struct<umass: double, npmi: double>>
per_topic_cv_coherence: struct<0: double, 1: double, 2: double, 3: double, 4: double, 5: double, 6: double, 7: double, 8: double, 9: double, 10: double, 11: double, 12: double, 13: double, 14: double, 15: double, 16: double, 17: double, 18: double, 19: double, 20: double, 21: double, 22: double, 23: double, 24: double, 25: double, 26: double, 27: double, 28: double, 29: double, 30: double, 31: double, 32: double, 33: double, 34: double, 35: double, 36: double, 37: double, 38: double, 39: double, 40: double, 41: double, 42: double, 43: double, 44: double, 45: double, 46: double, 47: double, 48: double, 49: double, 50: double>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 563, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              version: string
              created: string
              k_natural: int64
              total_docs: int64
              outliers_final: int64
              config: struct<dedup_threshold: double, min_keywords_safe: int64, mmr_diversity: double, umap_n_neighbors: int64, hdbscan_min_cluster_size: int64>
              metrics: struct<topic_diversity: double, inverted_rbo: double, silhouette_score: double, calinski_harabasz: double, davies_bouldin: double, outlier_ratio: double, mean_size: double, std_size: double, min_size: int64, max_size: int64, gini_coefficient: double, n_topics: int64, mean_distance: double, min_distance: double, max_distance: double, std_distance: double, we_centroid_distance: double, we_pd_mean: double, we_pd_min: double, we_pd_max: double, we_pd_std: double, pairwise_jaccard_distance: double, mean_umass_coherence: double, mean_npmi_coherence: double, mean_cv_coherence: double>
              features: struct<model_saved: bool, all_visualizations: bool, hierarchical_topics: bool, temporal_analysis: bool, outlier_reduction: bool, mathematical_metrics: bool>
              vs
              global: struct<topic_diversity: double, inverted_rbo: double, silhouette_score: double, calinski_harabasz: double, davies_bouldin: double, outlier_ratio: double, mean_size: double, std_size: double, min_size: int64, max_size: int64, gini_coefficient: double, n_topics: int64, mean_distance: double, min_distance: double, max_distance: double, std_distance: double, we_centroid_distance: double, we_pd_mean: double, we_pd_min: double, we_pd_max: double, we_pd_std: double, pairwise_jaccard_distance: double, mean_umass_coherence: double, mean_npmi_coherence: double, mean_cv_coherence: double>
              per_topic_coherence: struct<0: struct<umass: double, npmi: double>, 1: struct<umass: double, npmi: double>, 2: struct<umass: double, npmi: double>, 3: struct<umass: double, npmi: double>, 4: struct<umass: double, npmi: double>, 5: struct<umass: double, npmi: double>, 6: struct<umass: double, npmi: double>, 7: struct<umass: double, npmi: double>, 8: struct<umass: double, npmi: double>, 9: struct<umass: double, npmi: double>, 10: struct<umass: double, npmi: double>, 11: struct<umass: double, npmi: double>, 12: struct<umass: double, npmi: double>, 13: struct<umass: double, npmi: double>, 14: struct<umass: double, npmi: double>, 15: struct<umass: double, npmi: double>, 16: struct<umass: double, npmi: double>, 17: struct<umass: double, npmi: double>, 18: struct<umass: double, npmi: double>, 19: struct<umass: double, npmi: double>, 20: struct<umass: double, npmi: double>, 21: struct<umass: double, npmi: double>, 22: struct<umass: double, npmi: double>, 23: struct<umass: double, npmi: double>, 24: struct<umass: double, npmi: double>, 25: struct<umass: double, npmi: double>, 26: struct<umass: double, npmi: double>, 27: struct<umass: double, npmi: double>, 28: struct<umass: double, npmi: double>, 29: struct<umass: double, npmi: double>, 30: struct<umass: double, npmi: double>, 31: struct<umass: double, npmi: double>, 32: struct<umass: double, npmi: double>, 33: struct<umass: double, npmi: double>, 34: struct<umass: double, npmi: double>, 35: struct<umass: double, npmi: double>, 36: struct<umass: double, npmi: double>, 37: struct<umass: double, npmi: double>, 38: struct<umass: double, npmi: double>, 39: struct<umass: double, npmi: double>, 40: struct<umass: double, npmi: double>, 41: struct<umass: double, npmi: double>, 42: struct<umass: double, npmi: double>, 43: struct<umass: double, npmi: double>, 44: struct<umass: double, npmi: double>, 45: struct<umass: double, npmi: double>, 46: struct<umass: double, npmi: double>, 47: struct<umass: double, npmi: double>, 48: struct<umass: double, npmi: double>, 49: struct<umass: double, npmi: double>, 50: struct<umass: double, npmi: double>>
              per_topic_cv_coherence: struct<0: double, 1: double, 2: double, 3: double, 4: double, 5: double, 6: double, 7: double, 8: double, 9: double, 10: double, 11: double, 12: double, 13: double, 14: double, 15: double, 16: double, 17: double, 18: double, 19: double, 20: double, 21: double, 22: double, 23: double, 24: double, 25: double, 26: double, 27: double, 28: double, 29: double, 30: double, 31: double, 32: double, 33: double, 34: double, 35: double, 36: double, 37: double, 38: double, 39: double, 40: double, 41: double, 42: double, 43: double, 44: double, 45: double, 46: double, 47: double, 48: double, 49: double, 50: double>

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🏆 BERTopic v22 - THE COMPLETE MASTERPIECE

Mejoras sobre v21:

MODELO GUARDADO: SafeTensors para reutilización
TODAS LAS VISUALIZACIONES: DataMapPlot, Hierarchy, Heatmap, etc.
MÉTRICAS MATEMÁTICAS: Coherence (UMass, NPMI), Diversity, Silhouette
TOPICS OVER TIME: Análisis temporal
HIERARCHICAL TOPICS: Árbol jerárquico
GET_DOCUMENT_INFO: Metadata completa
REDUCE_OUTLIERS: Reducción inteligente

Métricas Matemáticas:

Topic Diversity: 0.6995305164319249
Silhouette Score: 0.5787373781204224
Mean NPMI Coherence: 0.0
Outlier Ratio: 0.14259485924112608

Archivos Generados:

modelos/topic_model_v22/: Modelo serializado (SafeTensors)
visualizaciones/: HTMLs interactivos (DataMapPlot, Hierarchy, etc.)
metricas/: JSON con todas las métricas
document_info_complete.xlsx: Metadata por documento
hierarchical_topics.xlsx: Estructura jerárquica
topics_over_time.xlsx: Evolución temporal

Cómo Cargar el Modelo:

from bertopic import BERTopic
topic_model = BERTopic.load("modelos/topic_model_v22")

Fórmulas Matemáticas Usadas:

UMass Coherence (Mimno et al., 2011)

C_UMass = (2 / (N * (N-1))) * Σ log((D(w_i, w_j) + ε) / D(w_j))

NPMI Coherence

NPMI(w_i, w_j) = log(P(w_i, w_j) / (P(w_i) * P(w_j))) / (-log(P(w_i, w_j)))

Topic Diversity (Dieng et al., 2020)

TD = |unique_words| / (|topics| * top_n)

Silhouette Coefficient

s(i) = (b(i) - a(i)) / max(a(i), b(i))

Davies-Bouldin Index

DB = (1/k) * Σ max_{j≠i}((σ_i + σ_j) / d(c_i, c_j))

Timestamp: 2026-01-14 16:26:35.120866

Downloads last month: 11

Size of downloaded dataset files:

1.5 kB

Size of the auto-converted Parquet files:

19.2 kB

Number of rows: