metadata

language:
  - es
  - fr
  - it
  - cs
  - pt
  - en
  - ar
  - fi
  - de
  - ru
  - hu
  - zh
license: cc-by-4.0
tags:
  - multilingual
  - bert
  - poetry
  - digital humanities
pipeline_tag: fill-mask
widget:
  - text: ¿Qué es la vida? Un [MASK].
datasets:
  - linhd-postdata/pulpo
metrics:
  - accuracy
library_name: transformers

Model Card for Aʟʙᴇʀᴛɪ:

Aʟʙᴇʀᴛɪ is the first multilingual domain-specific language model for poetry analysis.

Model Details

Model Description

As a pre-trained language model, it is trained using the masked language modeling objective on top of multilingual BERT, and therefore it needs to be fine-tuned to specific tasks.

Developed by: Javier de la Rosa, and Álvaro Pérez Pozo
Shared by: Javier de la Rosa
Model type: bert
Language(s) (NLP): Spanish, French, Italian, Czech, Portuguese, English, Arabic, Finnish, German, Russian, Hungarian, Chinese.
License: CC-BY 4.0
Finetuned from model: bert-base-multilingual-cased

Model Sources [optional]

Paper: https://arxiv.org/abs/2307.01387
Demo: https://huggingface.co/spaces/linhd-postdata/alberti

Uses

Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

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Training Hyperparameters

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Evaluation

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Factors

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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

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Technical Specifications [optional]

Model Architecture and Objective

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Software

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Citation [optional]

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