DiagTrast-xlm-roberta-base

Model Card for "DiagTrast-xlm-roberta-base"

This model is a fine-tuned version of xlm-roberta-base that is a multilingual version of RoBERTa and it is pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages.

DiagTrast-xlm-roberta-base was trained with 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 through the bidirectional learning of the sentences that the xlm-roberta-base model has.

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-xlm-roberta-base 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

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How to Get Started with the Model

Use the code below to get started with the model.

>>> from transformers import pipeline
>>> classifier = pipeline("text-classification", model='hackathon-somos-nlp-2023/DiagTrast-xlm-roberta-base')
>>> 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': 0.7664140462875366},
 {'label': 'Trastornos de la personalidad esquizotípica',
  'score': 0.9502732157707214},
 {'label': 'Trastornos de la personalidad antisocial',
  'score': 0.9722056984901428},
 {'label': 'Trastornos de la personalidad antisocial',
  'score': 0.49087557196617126}]

Training Details

Training Data

We use the 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

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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

Model Examination [optional]

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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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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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Team members

• Alberto Martín Garrido
• Edgar Mencia
• Miguel Ángel Solís Orozco
• Jose Carlos Vílchez Villegas