---
datasets:
- hackathon-somos-nlp-2023/DiagTrast
language:
- es
metrics:
- accuracy
license: mit
---

# Model Card for "DiagTrast-Berto"

This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) that is a BERT model trained on a big Spanish corpus.

DiagTrast-Berto was trained with [hackathon-somos-nlp-2023/DiagTrast](https://huggingface.co/datasets/hackathon-somos-nlp-2023/DiagTrast) dataset to classify statements with each of the 5 selected mental disorders of the DSM-5. While this task is classically approached with neural network-based models, the goal of implementing a transformer model is that instead of basing the classification criteria on keyword search, it is expected to understand natural language.

## Uses

The model can be used to classify statements written by professionals who have detected unusual behaviors or characteristics in their patients that would indicate the presence of a mental disorder; at the moment it only provides support for five of the disorders described in the DSM-5. It should be noted that the model aims to identify the predominant disorder, so it would be part of the professional's job to group the symptoms before entering them into the model for cases in which multiple disorders are presumed to be present at the same time.

### Direct Use

DiagTrast-Berto is already a fine-tuned model so it could be used directly to classify the statements.

### Out-of-Scope Use

This model should not be used as a replacement for a mental health professional because it is always necessary that each situation be evaluated responsibly and using all human intellectual capacity. Initially this model is designed as an auxiliary tool to facilitate the use of the DSM-5 by health professionals.

## Bias, Risks, and Limitations

The main limitation of the model is that it is restricted to the identification of only 5 of the DSM-5 disorders.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

[More Information Needed]

## How to Get Started with the Model

Use the code below to get started with the model.

```python
>>> from transformers import pipeline
>>> classifier = pipeline("text-classification", model='hackathon-somos-nlp-2023/DiagTrast-Berto')
>>> text = ["Gasta más dinero de lo que tiene, a menudo, su falta de control hace que esté en deudas", 
        "Le gusta estar solo y le molesta la gente a su alrededor, solo piensa en él",
        "Tiene pocas habilidades sociales, ignora normas de convivencia", 
        "Siempre que está en falta, culpa a los demás de sus problemas" ]
>>> classifier.predict(text)
[{'label': 'Trastornos de la personalidad antisocial',
  'score': 0.9967895150184631},
 {'label': 'Trastornos de la personalidad esquizotípica',
  'score': 0.9952175617218018},
 {'label': 'Trastornos de la personalidad antisocial',
  'score': 0.9772088527679443},
 {'label': 'Trastornos de la personalidad antisocial',
  'score': 0.855640172958374}]
```

## Training Details

### Training Data

We use the [hackathon-somos-nlp-2023/DiagTrast](https://huggingface.co/datasets/hackathon-somos-nlp-2023/DiagTrast) dataset, it was split with 90% of records for the training set and 10% for the test set using the 'datasets' library of hugging face.

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
[More Information Needed]

#### Preprocessing [optional]

[More Information Needed]


#### 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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[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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:** Tesla T4 
- **Hours used:** 0.09 hours
- **Cloud Provider:** Google
- **Compute Region:** Spain
- **Carbon Emitted:** 0.005 kg C02

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Team members

- [Alberto Martín Garrido](https://huggingface.co/Stremie)
- [Edgar Mencia](https://huggingface.co/edmenciab)
- [Miguel Ángel Solís Orozco](https://huggingface.co/homosapienssapiens)
- [Jose Carlos Vílchez Villegas](https://huggingface.co/JCarlos)