readme: ---
  language: "en"
  datasets:
  - Shared task on Detecting Signs of Depression from Social Media Text at LT-EDI 2022-ACL 2022
  metrics:
  - Macro F1-Score
  ---
  
  # Roberta for depression signs detection
  
  This model is a fine-tuned version the <a href="https://huggingface.co/cardiffnlp/twitter-roberta-base">cardiffnlp/twitter-roberta-base</a> model. It has been trained using a recently published corpus: <a href="https://competitions.codalab.org/competitions/36410#learn_the_details">Shared task on Detecting Signs of Depression from Social Media Text at LT-EDI 2022-ACL 2022</a>. 
  
  The obtained macro f1-score is 0.54, on the development set of the competition.
  
  # Intended uses
  This model is trained to classify the given text into one of the following classes: *moderate*, *severe*, or *not depression*.
  It corresponds to a **multiclass classification** task.
  
  # How to use
  
  You can use this model directly with a pipeline for text classification:
  
  ```python
  >>> from transformers import pipeline
  >>> classifier = pipeline("text-classification", model="paulagarciaserrano/roberta-depression-detection")
  >>> your_text = "I am very sad."
  >>> classifier (your_text)
  ```
  
  # Training and evaluation data
  The **train** dataset characteristics are:
  
  <table>
    <tr>
      <th>Class</th>
      <th>Nº sentences</th>
      <th>Avg. document length (in sentences)</th>
      <th>Nº words</th>
      <th>Avg. sentence length (in words)</th>
    </tr>
    <tr>
      <th>not depression</th>
      <td>7,884</td>
      <td>4</td>
      <td>153,738</td>
      <td>78</td>
    </tr>
    <tr>
      <th>moderate</th>
      <td>36,114</td>
      <td>6</td>
      <td>601,900</td>
      <td>100</td>
    </tr>
    <tr>
      <th>severe</th>
      <td>9,911</td>
      <td>11</td>
      <td>126,140</td>
      <td>140</td>
    </tr>
  </table>
  
  Similarly, the **evaluation** dataset characteristics are:
  
  <table>
    <tr>
      <th>Class</th>
      <th>Nº sentences</th>
      <th>Avg. document length (in sentences)</th>
      <th>Nº words</th>
      <th>Avg. sentence length (in words)</th>
    </tr>
    <tr>
      <th>not depression</th>
      <td>3,660</td>
      <td>2</td>
      <td>10,980</td>
      <td>6</td>
    </tr>
    <tr>
      <th>moderate</th>
      <td>66,874</td>
      <td>29</td>
      <td>804,794</td>
      <td>349</td>
    </tr>
    <tr>
      <th>severe</th>
      <td>2,880</td>
      <td>8</td>
      <td>75,240</td>
      <td>209</td>
    </tr>
  </table>
  
  # Training hyperparameters
  The following hyperparameters were used during training:
  
  * learning_rate: 2e-05
  * evaluation_strategy: epoch
  * save_strategy: epoch
  * per_device_train_batch_size: 8
  * per_device_eval_batch_size: 8
  * num_train_epochs: 5
  * seed: 10
  * weight_decay: 0.01
  * metric_for_best_model: macro-f1