readme: ---
  language:
  - en
  license: mit  # Example: apache-2.0 or any license from https://huggingface.co/docs/hub/model-repos#list-of-license-identifiers
  tags:
  - text  # Example: audio
  - Twitter
  datasets:
  - CLPsych 2015  # Example: common_voice. Use dataset id from https://hf.co/datasets
  metrics:
  - accuracy, f1, precision, recall, AUC # Example: wer. Use metric id from https://hf.co/metrics
  
  model-index:
  - name: distilbert-depression-mixed
    results: []
  ---
  
  # distilbert-depression-mixed
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) trained on CLPsych 2015 and a scraped dataset, and evaluated on a scraped dataset from Twitter to detect potential users in Twitter for depression.
  It achieves the following results on the evaluation set:
  - Evaluation Loss: 0.71
  - Accuracy: 0.63
  - F1: 0.59
  - Precision: 0.66
  - Recall: 0.53
  - AUC: 0.63
  
  
  ## Intended uses & limitations
  
  Feed a corpus of tweets to the model to generate label if input is indicative of a depressed user or not. Label 1 is depressed, Label 0 is not depressed.
  
  Limitation: All token sequences longer than 512 are automatically truncated. Also, training and test data may be contaminated with mislabeled users.
  
  ### How to use
  
  You can use this model directly with a pipeline for sentiment analysis:
  
  ```python
  >>> from transformers import DistilBertTokenizerFast, AutoTokenizer
  >>> tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
  >>> from transformers import DistilBertForSequenceClassification
  >>> model = DistilBertForSequenceClassification.from_pretrained(r"distilbert-depression-mixed")
  >>> from transformers import pipeline
  >>> classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
  >>> tokenizer_kwargs = {'padding':True,'truncation':True,'max_length':512}
  >>> result=classifier('pain peko',**tokenizer_kwargs) #For truncation to apply in the pipeline
  >>> #Should note that the string passed as the input can be a corpus of tweets concatenated together into one document.
  
  
  [{'label': 'LABEL_1', 'score': 0.5048992037773132}]
  ```
  
  Otherwise, download the files and specify within the pipeline the path to the folder that contains the config.json, pytorch_model.bin, and training_args.bin
  
  ## Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 4.19e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - weight_decay: 0.06
  - num_epochs: 5.0
  
  ## Training results
  
  
  | Epoch | Training Loss | Validation Loss | Accuracy |    F1    | Precision |  Recall  |    AUC   |
  |:-----:|:-------------:|:---------------:|:--------:|:--------:|:---------:|:--------:|:--------:|
  | 1.0   | 0.68          | 0.66            | 0.61     | 0.54     | 0.60      | 0.50     | 0.60     |
  | 2.0   | 0.65          | 0.65            | 0.63     | 0.49     | 0.70      | 0.37     | 0.62     |
  | 3.0   | 0.53          | 0.63            | 0.66     | 0.58     | 0.69      | 0.50     | 0.65     |
  | 4.0   | 0.39          | 0.66            | 0.67     | 0.61     | 0.69      | 0.54     | 0.67     |
  | 5.0   | 0.27          | 0.72            | 0.65     | 0.61     | 0.63      | 0.60     | 0.64     |