
# Readability ES Sentences for two classes

Model based on the Roberta architecture finetuned on [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for readability assessment of Spanish texts.

## Description and performance

This version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model performs binary classification among the following classes:
- Simple.
- Complex.

It achieves a F1 macro average score of 0.8923, measured on the validation set.

## Model variants

- `readability-es-sentences` (this model). Two classes, sentence-based dataset.
- [`readability-es-paragraphs`](https://huggingface.co/hackathon-pln-es/readability-es-paragraphs). Two classes, paragraph-based dataset.
- [`readability-es-3class-sentences`](https://huggingface.co/hackathon-pln-es/readability-es-3class-sentences). Three classes, sentence-based dataset.
- [`readability-es-3class-paragraphs`](https://huggingface.co/hackathon-pln-es/readability-es-3class-paragraphs). Three classes, paragraph-based dataset.

## Datasets

- [`readability-es-hackathon-pln-public`](https://huggingface.co/datasets/hackathon-pln-es/readability-es-hackathon-pln-public), composed of:
  * coh-metrix-esp corpus.
  * Various text resources scraped from websites.
- Other non-public datasets: newsela-es, simplext.

## Training details

Please, refer to [this training run](https://wandb.ai/readability-es/readability-es/runs/3rgvwps0/overview) for full details on hyperparameters and training regime.

## Biases and Limitations

- Due to the scarcity of data and the lack of a reliable gold test set, performance metrics are reported on the validation set.
- One of the datasets involved is the Spanish version of newsela, which is frequently used as a reference. However, it was created by translating previous datasets, and therefore it may contain somewhat unnatural phrases.
- Some of the datasets used cannot be publicly disseminated, making it more difficult to assess the existence of biases or mistakes.
- Language might be biased towards the Spanish dialect spoken in Spain. Other regional variants might be sub-represented.
- No effort has been performed to alleviate the shortcomings and biases described in the [original implementation of BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish#bias-examples-spanish).

## Authors

- [Laura Vásquez-Rodríguez](https://lmvasque.github.io/)
- [Pedro Cuenca](https://twitter.com/pcuenq)
- [Sergio Morales](https://www.fireblend.com/)
- [Fernando Alva-Manchego](https://feralvam.github.io/)


{"language": "es", "license": "cc-by-4.0", "tags": ["spanish", "roberta", "bertin"], "pipeline_tag": "text-classification", "widget": [{"text": "La ciencia nos ense\u00f1a, en efecto, a someter nuestra raz\u00f3n a la verdad y a conocer y juzgar las cosas como son, es decir, como ellas mismas eligen ser y no como quisi\u00e9ramos que fueran."}]}
transformers pytorch roberta text-classification spanish bertin es license:cc-by-4.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #text-classification #spanish #bertin #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Readability ES Sentences for two classes

Model based on the Roberta architecture finetuned on BERTIN for readability assessment of Spanish texts.

## Description and performance

This version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model performs binary classification among the following classes:
- Simple.
- Complex.

It achieves a F1 macro average score of 0.8923, measured on the validation set.

## Model variants

- 'readability-es-sentences' (this model). Two classes, sentence-based dataset.
- 'readability-es-paragraphs'. Two classes, paragraph-based dataset.
- 'readability-es-3class-sentences'. Three classes, sentence-based dataset.
- 'readability-es-3class-paragraphs'. Three classes, paragraph-based dataset.

## Datasets

- 'readability-es-hackathon-pln-public', composed of:
  * coh-metrix-esp corpus.
  * Various text resources scraped from websites.
- Other non-public datasets: newsela-es, simplext.

## Training details

Please, refer to this training run for full details on hyperparameters and training regime.

## Biases and Limitations

- Due to the scarcity of data and the lack of a reliable gold test set, performance metrics are reported on the validation set.
- One of the datasets involved is the Spanish version of newsela, which is frequently used as a reference. However, it was created by translating previous datasets, and therefore it may contain somewhat unnatural phrases.
- Some of the datasets used cannot be publicly disseminated, making it more difficult to assess the existence of biases or mistakes.
- Language might be biased towards the Spanish dialect spoken in Spain. Other regional variants might be sub-represented.
- No effort has been performed to alleviate the shortcomings and biases described in the original implementation of BERTIN.

## Authors

- Laura Vásquez-Rodríguez
- Pedro Cuenca
- Sergio Morales
- Fernando Alva-Manchego


# Readability ES Sentences for two classes

Model based on the Roberta architecture finetuned on BERTIN for readability assessment of Spanish texts. ## Description and performance

This version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model performs binary classification among the following classes:
- Simple.
- Complex.

It achieves a F1 macro average score of 0.8923, measured on the validation set. ## Model variants

- 'readability-es-sentences' (this model). Two classes, sentence-based dataset.
- 'readability-es-paragraphs'. Two classes, paragraph-based dataset.
- 'readability-es-3class-sentences'. Three classes, sentence-based dataset.
- 'readability-es-3class-paragraphs'. Three classes, paragraph-based dataset. ## Datasets

- 'readability-es-hackathon-pln-public', composed of:
  * coh-metrix-esp corpus.
  * Various text resources scraped from websites.
- Other non-public datasets: newsela-es, simplext. ## Training details

Please, refer to this training run for full details on hyperparameters and training regime. ## Biases and Limitations

- Due to the scarcity of data and the lack of a reliable gold test set, performance metrics are reported on the validation set.
- One of the datasets involved is the Spanish version of newsela, which is frequently used as a reference. However, it was created by translating previous datasets, and therefore it may contain somewhat unnatural phrases.
- Some of the datasets used cannot be publicly disseminated, making it more difficult to assess the existence of biases or mistakes.
- Language might be biased towards the Spanish dialect spoken in Spain. Other regional variants might be sub-represented.
- No effort has been performed to alleviate the shortcomings and biases described in the original implementation of BERTIN. ## Authors

- Laura Vásquez-Rodríguez
- Pedro Cuenca
- Sergio Morales
- Fernando Alva-Manchego
TAGS
#transformers #pytorch #roberta #text-classification #spanish #bertin #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Readability ES Sentences for two classes

Model based on the Roberta architecture finetuned on BERTIN for readability assessment of Spanish texts. ## Description and performance

This version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model performs binary classification among the following classes:
- Simple.
- Complex.

It achieves a F1 macro average score of 0.8923, measured on the validation set. ## Model variants

- 'readability-es-sentences' (this model). Two classes, sentence-based dataset.
- 'readability-es-paragraphs'. Two classes, paragraph-based dataset.
- 'readability-es-3class-sentences'. Three classes, sentence-based dataset.
- 'readability-es-3class-paragraphs'. Three classes, paragraph-based dataset. ## Datasets

- 'readability-es-hackathon-pln-public', composed of:
  * coh-metrix-esp corpus.
  * Various text resources scraped from websites.
- Other non-public datasets: newsela-es, simplext. ## Training details

Please, refer to this training run for full details on hyperparameters and training regime. ## Biases and Limitations

- Due to the scarcity of data and the lack of a reliable gold test set, performance metrics are reported on the validation set.
- One of the datasets involved is the Spanish version of newsela, which is frequently used as a reference. However, it was created by translating previous datasets, and therefore it may contain somewhat unnatural phrases.
- Some of the datasets used cannot be publicly disseminated, making it more difficult to assess the existence of biases or mistakes.
- Language might be biased towards the Spanish dialect spoken in Spain. Other regional variants might be sub-represented.
- No effort has been performed to alleviate the shortcomings and biases described in the original implementation of BERTIN. ## Authors

- Laura Vásquez-Rodríguez
- Pedro Cuenca
- Sergio Morales
- Fernando Alva-Manchego
TAGS
#transformers #pytorch #roberta #text-classification #spanish #bertin #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Readability ES Sentences for two classes

Model based on the Roberta architecture finetuned on BERTIN for readability assessment of Spanish texts.## Description and performance

This version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model performs binary classification among the following classes:
- Simple.
- Complex.

It achieves a F1 macro average score of 0.8923, measured on the validation set.## Model variants

- 'readability-es-sentences' (this model). Two classes, sentence-based dataset.
- 'readability-es-paragraphs'. Two classes, paragraph-based dataset.
- 'readability-es-3class-sentences'. Three classes, sentence-based dataset.
- 'readability-es-3class-paragraphs'. Three classes, paragraph-based dataset.## Datasets

- 'readability-es-hackathon-pln-public', composed of:
  * coh-metrix-esp corpus.
  * Various text resources scraped from websites.
- Other non-public datasets: newsela-es, simplext.## Training details

Please, refer to this training run for full details on hyperparameters and training regime.## Biases and Limitations

- Due to the scarcity of data and the lack of a reliable gold test set, performance metrics are reported on the validation set.
- One of the datasets involved is the Spanish version of newsela, which is frequently used as a reference. However, it was created by translating previous datasets, and therefore it may contain somewhat unnatural phrases.
- Some of the datasets used cannot be publicly disseminated, making it more difficult to assess the existence of biases or mistakes.
- Language might be biased towards the Spanish dialect spoken in Spain. Other regional variants might be sub-represented.
- No effort has been performed to alleviate the shortcomings and biases described in the original implementation of BERTIN.## Authors

- Laura Vásquez-Rodríguez
- Pedro Cuenca
- Sergio Morales
- Fernando Alva-Manchego