modernisa-byt5-base

Model Card for modernisa-byt5-base

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This model translates from historical, non-normalized Spanish with historical orthography to modern normalized Spanish. It is a fine-tuned version of the multilingual version of the text-totext transformer ByT5 (Xue et al, 2021, 2022) fro translation from 17th century Spanish to modern Spanish.

<!-- # Table of Contents - [Model Card for modernisa-byt5-base](#model-card-for--model_id-) - [Table of Contents](#table-of-contents) - [Table of Contents](#table-of-contents-1) - [Model Details](#model-details) - [Model Description](#model-description) - [Uses](#uses) - [Direct Use](#direct-use) - [Downstream Use [Optional]](#downstream-use-optional) - [Out-of-Scope Use](#out-of-scope-use) - [Bias, Risks, and Limitations](#bias-risks-and-limitations) - [Recommendations](#recommendations) - [Training Details](#training-details) - [Training Data](#training-data) - [Training Procedure](#training-procedure) - [Preprocessing](#preprocessing) - [Speeds, Sizes, Times](#speeds-sizes-times) - [Evaluation](#evaluation) - [Testing Data, Factors & Metrics](#testing-data-factors--metrics) - [Testing Data](#testing-data) - [Factors](#factors) - [Metrics](#metrics) - [Results](#results) - [Model Examination](#model-examination) - [Environmental Impact](#environmental-impact) - [Technical Specifications [optional]](#technical-specifications-optional) - [Model Architecture and Objective](#model-architecture-and-objective) - [Compute Infrastructure](#compute-infrastructure) - [Hardware](#hardware) - [Software](#software) - [Citation](#citation) - [Glossary [optional]](#glossary-optional) - [More Information [optional]](#more-information-optional) - [Model Card Authors [optional]](#model-card-authors-optional) - [Model Card Contact](#model-card-contact) - [How to Get Started with the Model](#how-to-get-started-with-the-model) -->

Model Details

Model Description

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This model translates from historical, non-normalized Spanish with historical orthography to modern normalized Spanish. It is a fine-tuned version of the multilingual version of the text-to-text transformer ByT5 (Xue et al, 2021, 2022) for translation from 17th century Spanish to modern Spanish. A fine-tuned version of google/byt5-base trained on a parallel corpus of 44 Spanish-language Golden Age dramas.

• Developed by: Javier de la Rosa
• Shared by [Optional]: More information needed
• Model type: Transformer
• Language(s) (NLP): es
• License: apache-2.0
• Parent Model: ByT5-Base
• Resources for more information: More information needed
  • GitHub Repo
  • Associated Paper
  • Demo

Uses

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The motivation to develop the model was to provide a tool producing normalized text which enables computational analyses (such as distances between texts, clustering, topic modeling, sentiment analysis, stylometry etc.), to facilitate modern editions of historical texts and thus alleviate a job which been done manually so far and to provide a resource which may be used by historians and editors who manually transcribe texts produced in the 17th century which were not yet digitized, which are available in cultural heritage institutions, especially libraries and archives. While all the dramas used are written in verses, the model was not tested on texts in prose; the quality of the translation of prose texts into modern normalized Spanish might therefore differ significantly from the satisfying results achieved with dramas in verses.

Direct Use

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This resource may be used by historians and editors who manually transcribe texts produced in the 17th century which were not yet digitized and which are typically available in cultural heritage institutions, especially libraries and archives.

Downstream Use [Optional]

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This model is already fine-tuned.

Out-of-Scope Use

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Bias, Risks, and Limitations

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It has to be underlined that the parallel corpus was created solely from text written by four men who lived in counter-reformatory Spain during the rule of inquisition. The view of the world of these dramatists is from our contemporary point of view outdated, strongly patriarchal, misogynist and discriminatory with respect to non-catholic human beings.

Recommendations

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The intended users of this model are researchers and editors of historical texts. We cannot imagine any harm done by the modernization of those texts as a technical process; however, the reading of such texts may be harmful for persons who are not acquainted with the worldview produced in 17th century Spain. Moreover, linguistic change provides a strong challenge to Natural Language Processing (NLP) applications. Vis-à-vis other languages, linguistic change within the Spanish language was not very pronounced. Further research on the modernization of historical languages is therefore strongly recommended.

Training Details

Training Data

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We built a parallel corpus of Spanish Golden Age theater texts with pairs of 44 Golden Age dramas in historical orthography and current orthography. Both corpora were aligned line by line to establish a ground truth for the translation between the different historical varieties of Spanish. The 44 dramas have been written by Juan Ruiz de Alarcón (5), Pedro Calderón de la Barca (28), Félix Lope de Vega Carpio (6), and Juan Pérez de Montalbán (5). The dataset is available on Huggingface.

Training Procedure

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Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Framework versions

• Transformers 4.13.0.dev0
• Pytorch 1.10.0+cu111
• Datasets 1.15.2.dev0
• Tokenizers 0.10.3

Preprocessing

Speeds, Sizes, Times

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After randomizing all 141,023 lines in the corpus, we split it into training (80%), validation (10%) and test (10%) sets stratifying by play. We then fine-tuned T5 and ByT5 base models on sequence lengths of 256 doing a grid search for 3 and 5 epochs, weight decay 0 and 0.01, learning rates of 0.001 and 0.0001, and with and without a “translate” prompt.

Evaluation

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Testing Data, Factors & Metrics

Testing Data

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A single drama by Lope de Vega (Castelvines y Monteses, 1647).

Factors

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More information needed

Metrics

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More information needed

Results

BLEU: 80.66 CER: 4.20%

Model Examination

More information needed

Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: More information needed
• Hours used: More information needed
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• Carbon Emitted: More information needed

Technical Specifications [optional]

Model Architecture and Objective

More information needed

Compute Infrastructure

More information needed

Hardware

More information needed

Software

More information needed

Citation

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BibTeX:

@inproceedings{de_la_rosa_modernilproject_2022,
	address = {Tokyo},
	title = {The {Moderniſa} {Project}: {Orthographic} {Modernization} of {Spanish} {Golden} {Age} {Dramas} with {Language} {Models}},
	shorttitle = {The {Moderniſa} {Project}},
	url = {https://dh2022.dhii.asia/abstracts/files/DE_LA_ROSA_Javier_The_Moderni_a_Project__Orthographic_Modern.html},
	language = {en},
	publisher = {Alliance of Digital Humanities Organizations ADHO / The University of Tokyo, Japan},
	author = {De la Rosa, Javier and Cuéllar, Álvaro and Lehmann, Jörg},
	month = jul,
	year = {2022},
}

APA:

De la Rosa, J., Cuéllar, Á., & Lehmann, J. (2022, July). The Moderniſa Project: Orthographic Modernization of Spanish Golden Age Dramas with Language Models. Retrieved from https://dh2022.dhii.asia/abstracts/files/DE_LA_ROSA_Javier_The_Moderni_a_Project__Orthographic_Modern.html

MLA:

De la Rosa, Javier, et al. The Moderniſa Project: Orthographic Modernization of Spanish Golden Age Dramas with Language Models. Alliance of Digital Humanities Organizations ADHO / The University of Tokyo, Japan, 2022, https://dh2022.dhii.asia/abstracts/files/DE_LA_ROSA_Javier_The_Moderni_a_Project__Orthographic_Modern.html.

Glossary [optional]

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More information needed

More Information [optional]

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Model Card Authors [optional]

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Javier de la Rosa, Jörg Lehmann, questions and comments about the model card can be directed to Jörg Lehmann at [email protected]

Model Card Contact

Jörg Lehmann

How to Get Started with the Model

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