# Model Card for Model ID (in progress of completing)
This model is a fine-tunning of BETO uncase to detect offensive and discriminatory language against lgbt community. 
It could be used as a moderation service in forums and digital spaces.
## Model Card Contact
[cmartinez@ucsc.cl]

## Model Details

### Model description process
-Starting recovering of discriminatory phrases for the LGBTQIA+ community from X/Twitter, Instagram and Tiktok (197 phrases) .
-Labelling by 3 raters as non-lgbtphobic (0) and lgbtphobic (1).
-Text augmentation was applied backtranslation and random synonyms replacing.
-Translating to Spanish part of McGiff, J., & Nikolov, N. S. (2024) dataset and added (under  licence CC-BY-4.0)
-Finally, we obtained 1234 tagged phrases for version 1.0.1 of LGBTQIAphobia_augmented. Please cite data set as:

Martínez-Araneda, C., Maldonado Montiel, D., Gutiérrez Valenzuela, M., Gómez Meneses, P., Segura Navarrete, A., & Vidal-Castro, C. (2024). 
LGBTQIAphobia dataset (augmented) (1.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14563166

- **Developed by:** [Martínez-Araneda, C; Segura Navarrete, A.; Gutierrez Valenzuela, Mariella; Maldonado Mintiel, Diego; Gómez Meneses, P.; Vidal-Castro; Christian  ]
- **Model type:** [text-classification]
- **Language(s) (NLP):** [Spanish]
- **License:** [CC-BY-4.0]
- **Finetuned from model [dccuchile/bert-base-spanish-wwm-uncased]:** More information of base model [https://github.com/dccuchile/beto]

### Model Sources [optional]

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## Uses

This model can be used to detect offensive and discriminatory language against lgbt community. It could be used as a moderation service in forums and digital spaces.

### Direct Use

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

This model has its own bias from having been adjusted with a small data set.<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations
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# How to Get Started with the Model
#libraries
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# Define la ruta de donde cargarás el modelo
#load_directory = "./lgbetO"
# Cargar el modelo entrenado
#model = AutoModelForSequenceClassification.from_pretrained(load_directory)
# Cargar el tokenizer
#tokenizer = AutoTokenizer.from_pretrained(load_directory)
# Training Details

The training process begins by retrieving offensive/non-offensive and discriminatory/non-discriminatory language against phrases related to the lgbt community from twitter, instagram and tiktok, 
preprocessing them, labeling them by 3 raters, augmenting them with backtranslation and synonyms, 
and adjusting the BETO base model (dccuchile/bert-base -spanish-wwm-uncased) for discriminatory phrase detection for the lgbt community.

### Training Data

Citation
Martínez-Araneda, C., Maldonado Montiel, D., Gutiérrez Valenzuela, M., Gómez Meneses, P., Segura Navarrete, A., & Vidal-Castro, C. (2024). 
LGBTQIAphobia dataset (augmented) (1.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14563166

### 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:** Google Cloud Platform [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** southamerica
- **Carbon Emitted:** 0.14kgCO$_2$eq/kWh


Experiments were conducted using Google Cloud Platform in region southamerica-east1, which has a carbon efficiency of 0.2 kgCO$_2$eq/kWh. A cumulative of 10 hours of computation was performed on hardware of type T4 (TDP of 70W).

Total emissions are estimated to be 0.14 kgCO$_2$eq of which 100 percents were directly offset by the cloud provider.
    

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure
(GPU) del backend de Google Compute Engine en Python 3

#### Hardware
RAM: 3.87 GB/12.67 GB
Disco: 33.96 GB/112.64 GB

#### 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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## Model Card Authors [optional]

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