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 that is a BERT model trained on a big Spanish corpus.
DiagTrast-Berto 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.
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
[More Information Needed]
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-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 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
[More Information Needed]
Preprocessing [optional]
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Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
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Evaluation
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
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- 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
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
BibTeX:
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APA:
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