---
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](https://huggingface.co/bert-base-multilingual-cased), and therefore **it needs to be fine-tuned to specific tasks**.

- **Developed by:** [Javier de la Rosa](https://huggingface.co/versae), and [Álvaro Pérez Pozo](https://huggingface.co/)
- **Shared by:** [Javier de la Rosa](https://huggingface.co/versae)
- **Model type:** `bert`
- **Language(s) (NLP):** Spanish, French, Italian, Czech, Portuguese, English, Arabic, Finnish, German, Russian, Hungarian, Chinese.
- **License:** [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/)
- **Finetuned from model:** [`bert-base-multilingual-cased`](https://huggingface.co/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

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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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### 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



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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