Leaderboard ODESIA


Evaluation of language models in English and Spanish

Goals: to make a direct comparison between the effectiveness of language models in English and Spanish to measure the effectiveness gap.
Method: evaluation on the ODESIA Benchmark, a collection of Natural Language Processing tasks with comparable datasets in English and Spanish.

Goals

The ODESIA Leaderboard allows (I) to measure the effectiveness gap of Spanish language models with respect to English; (II) to comparatively evaluate Spanish language models. If you have developed a Spanish language model, submit your results!

For more details check here.

Results

The average effectiveness gap between Spanish and English is 20%, , with a standard error of +-4%. It should be noted that the gap is more pronounced in the most difficult tasks (up to over 200% in the most intrinsically difficult task), and therefore the average value has a relative representativeness.

For more details check here.

Tasks

Two sets of tasks are used: (I) ODESIA CORE, , bilingual tasks with private test data (this avoids contamination, that the models have seen the evaluation keys in the pre-training phase); and (II) ODESIA EXTENDED, which adds a set of standard and publicly available bilingual tasks.

For more details check here.

Methodology

ODESIA Leaderboard uses a set of bilingual tasks to compare the state of the art in English and Spanish. On each task (I) the intrinsic difficulty is estimated by applying several non-linguistic algorithms and (II) the best results in each language are calibrated using that intrinsic difficulty.

For more details check here.

Leaderboard

Odesia Core Tasks

# System Arithmetic mean EXIST 2022: Sexism detection (ES) EXIST 2022: Sexism categorisation (ES) DIPROMATS 2023: Propaganda identification (ES) DIPROMATS 2023: Coarse propaganda characterization (ES) DIPROMATS 2023: Fine-grained propaganda characterization (ES) DIANN 2023: Disability detection (ES) EXIST-2023: Sexism identification (ES) EXIST-2023: Source Intention (ES) EXIST-2023: Sexism categorization (ES) SQAC-SQUAD 2024: Question answering (ES)
1 distilbert-base-multilingual-cased 0.459 0.72 0.47 0.75 0.34 0.09 0.78 0.57 0.36 0.29 0.22
2 distillbert-base-spanish-uncased 0.473 0.72 0.51 0.77 0.34 0.07 0.75 0.60 0.39 0.33 0.25
3 xlm-roberta-base 0.515 0.74 0.50 0.79 0.47 0.10 0.84 0.62 0.40 0.32 0.37
4 ixambert-base-cased 0.485 0.71 0.49 0.77 0.32 0.06 0.83 0.60 0.37 0.34 0.36
5 bert-base-multilingual-cased 0.488 0.72 0.47 0.78 0.35 0.10 0.84 0.60 0.37 0.33 0.32
6 bert-base-spanish-wwm-cased 0.524 0.72 0.54 0.79 0.44 0.14 0.81 0.63 0.39 0.37 0.41
7 PlanTL-GOB-ES-roberta-base-bne 0.521 0.74 0.56 0.81 0.42 0.12 0.75 0.63 0.40 0.37 0.41
8 bertin-roberta-base-spanish 0.493 0.73 0.49 0.76 0.36 0.08 0.75 0.62 0.39 0.33 0.42
9 PlanTL-GOB-ES-roberta-large-bne 0.552 0.75 0.57 0.82 0.44 0.24 0.82 0.64 0.40 0.38 0.46
10 xlm-roberta-large 0.564 0.77 0.56 0.82 0.47 0.26 0.84 0.64 0.42 0.40 0.46
# System Arithmetic mean EXIST 2022: Sexism detection (EN) EXIST 2022: Sexism categorisation (EN) DIANN 2023: Disability detection (EN) DIPROMATS 2023: Propaganda identification (EN) DIPROMATS 2023: Coarse propaganda characterization (EN) DIPROMATS 2023: Fine-grained propaganda characterization (EN) EXIST-2023: Sexism categorization (EN) EXIST-2023: Sexism identification (EN) EXIST-2023: Source intention (EN) SQAC-SQUAD 2024: Question answering (EN)
1 bert-base-multilingual-cased 0.501 0.76 0.50 0.73 0.80 0.48 0.18 0.34 0.60 0.32 0.30
2 distilbert-base-multilingual-cased 0.472 0.74 0.53 0.68 0.77 0.45 0.16 0.30 0.58 0.31 0.20
3 distilbert-base-uncased 0.497 0.77 0.55 0.66 0.78 0.47 0.14 0.37 0.62 0.34 0.27
4 bert-base-cased 0.513 0.76 0.53 0.72 0.81 0.50 0.21 0.37 0.61 0.32 0.30
5 ixambert-base-cased 0.503 0.75 0.53 0.73 0.78 0.49 0.14 0.36 0.61 0.32 0.32
6 xlm-roberta-base 0.517 0.76 0.53 0.76 0.80 0.54 0.16 0.35 0.62 0.32 0.33
7 roberta-base 0.530 0.78 0.53 0.75 0.81 0.52 0.19 0.38 0.63 0.33 0.38
8 xlm-roberta-large 0.565 0.79 0.56 0.78 0.81 0.52 0.39 0.39 0.63 0.36 0.42
9 roberta-large 0.587 0.81 0.58 0.79 0.82 0.55 0.47 0.40 0.64 0.35 0.46

Odesia Extended Tasks

# System Arithmetic mean MLDOC 2018: Document classification (ES) Multilingual Complex Named Entity Recognition 2022 (ES) SQAC-SQUAD 2016: Question answering (ES) Semantic Textual Similarity 2017 (ES) DIANN 2018: Negation detection (ES)
1 xlm-roberta-base 0.772 0.95 0.66 0.67 0.73 0.85
2 xlm-roberta-large 0.832 0.96 0.71 0.77 0.80 0.92
3 bert-base-multilingual-cased 0.750 0.96 0.64 0.71 0.70 0.74
4 distilbert-base-multilingual-cased 0.724 0.94 0.61 0.55 0.69 0.83
5 PlanTL-GOB-ES-roberta-base-bne 0.792 0.96 0.64 0.74 0.75 0.87
6 PlanTL-GOB-ES-roberta-large-bne 0.730 0.96 0.63 0.77 0.76 0.53
7 bertin-roberta-base-spanish 0.772 0.96 0.62 0.73 0.67 0.88
8 bert-base-spanish-wwm-cased 0.810 0.96 0.63 0.71 0.79 0.96
9 distillbert-base-spanish-uncased 0.724 0.96 0.61 0.53 0.74 0.78
10 ixambert-base-cased 0.768 0.96 0.63 0.71 0.81 0.73
# Sistema Media aritmética MLDOC 2018: Document classification (EN) Multilingual Complex Named Entity Recognition 2022 (EN) SQAC-SQUAD 2016: Question answering (EN) Semantic Textual Similarity 2017 (EN) DIANN 2018: Negation detection (EN)
1 ixambert-base-cased 0.804 0.98 0.65 0.80 0.82 0.77
2 bert-base-cased 0.784 0.97 0.68 0.78 0.82 0.67
3 distilbert-base-uncased 0.800 0.97 0.67 0.77 0.81 0.78
4 roberta-large 0.864 0.98 0.75 0.88 0.86 0.85
5 roberta-base 0.852 0.98 0.70 0.85 0.85 0.88
6 distilbert-base-multilingual-cased 0.774 0.97 0.63 0.75 0.76 0.76
7 xlm-roberta-large 0.868 0.98 0.74 0.86 0.84 0.92
8 xlm-roberta-base 0.808 0.98 0.69 0.80 0.80 0.77
9 bert-base-multilingual-cased 0.784 0.97 0.67 0.81 0.80 0.67

Check all the results on the Leaderboard

Gap Spanish-English

La brecha total entre el español y el inglés es del 21%


Odesia Core Tasks


Tasks Best result Spanish Best result English
Media total 0.60 0.60 14%
EXIST 2022: Sexism detection (ES) 0.77 0.81 17%
EXIST 2022: Sexism categorisation (ES) 0.57 0.58 10%
DIPROMATS 2023: Propaganda identification (ES) 0.82 0.82 11%
DIPROMATS 2023: Coarse propaganda characterization (ES) 0.47 0.55 48%
DIPROMATS 2023: Fine-grained propaganda characterization (ES) 0.26 0.47 299%
DIANN 2023: Disability detection (ES) 0.84 0.79 1%
EXIST-2023: Sexism identification (ES) 0.64 0.64 10%
EXIST-2023: Source Intention (ES) 0.42 0.36 -4%
EXIST-2023: Sexism categorization (ES) 0.40 0.40 12%
SQAC-SQUAD 2024: Question answering (ES) 0.46 0.46 19%

Odesia Extended Tasks


Tasks Best result Spanish Best result English
Total mean 0.84 0.88 35.2%
MLDOC 2018: Document classification (ES) 0.96 0.98 40%
Multilingual Complex Named Entity Recognition 2022 (ES) 0.71 0.75 5%
SQAC-SQUAD 2016: Question answering (ES) 0.77 0.88 25%
Semantic Textual Similarity 2017 (ES) 0.81 0.86 13%
DIANN 2018: Negation detection (ES) 0.96 0.92 93%

Check all the results on the Leaderboard

Participate

You can participate in several ways:

(1) Evaluando modelos de lenguaje en español o en inglés.
(2) Evaluando modelos multilenguajes en español e inglés.

If you want to evaluate your model for a single task, you can do so on the EvALL.

Register and participate by sending us your results.