alberti-bert-base-multilingual-cased

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]

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

Uses

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Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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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

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

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Environmental Impact

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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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Hardware

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Software

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Citation [optional]

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Glossary [optional]

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