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--- |
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library_name: transformers |
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tags: [] |
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--- |
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# **TriageTron-large** |
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TriageTron-large se trata de un modelo inicializado con [xlnet-large-unsased](https://huggingface.co/xlnet/xlnet-large-cased) y adaptado con preguntas y especialidades |
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para poder realizar Derivaciones Automatizadas en Servicios Hospitalarios. El dataset se puede encontrar de manera pública y se trata de [MedDialog EN](https://arxiv.org/abs/2004.03329). |
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Para el entrenamiento de este modelo hemos seguidos los estándares de la librería [transformers](https://github.com/huggingface/transformers) y se ha utilizado una |
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NVIDIA P100. Además, ha sido entrenado con un batch-size de 4, un learning rate de 2e-5, X epochs y un weigth decay de 0.015, loggeando los resultados cada 100 iteraciones. |
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## **Utilización** |
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Mediante el `pipeline` de Hugging Face: |
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```python |
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from transformers import pipeline |
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model_id = "eperezs/TriageTron-large" |
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pipe = pipeline("text-classification", model_id, token = <your_token_here>) |
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text = "I have pain in the back" |
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result = pipe(text) |
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print(result) |
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``` |
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Mediante `AutoModelForSequenceClassification` y `AutoTokenizer`: |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model_id = "eperezs/TriageTron-large" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForSequenceClassification.from_pretrained(model_id) |
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text = "I have pain in the back" |
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inputs = tokenizer(text, return_tensors = "pt") |
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logits = model(**inputs) |
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print(f"The predicted class is {model.id2label[logits.argmax()]}") |
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print(result) |
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``` |
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## **Evaluación** |
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- **Accuracy** : 89,9% |
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- **F1**: 89% |
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- **Precision**: 89% |
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#### Training Hyperparameters |
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- **learning_rate**: 2e-5 |
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- **batch_size**: 4 |
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- **num_train_epochs**: 3 |
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- **weight_decay**: 0.015 |
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- **optimizer**: AdamW |
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- **test_size**: 0.2 |
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- **logging_steps**: 100 |
