---
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-base
  results: []
---

# distilbert-depression-base

This model is a fine-tuned version of [base-uncased](https://huggingface.co/distilbert-base-uncased) trained on CLPsych 2015 and evaluated on a scraped dataset from Twitter.
It achieves the following results on the evaluation set:
- Evaluation Loss: 0.64
- Accuracy: 0.65
- F1: 0.70
- Precision: 0.61
- Recall: 0.83
- AUC: 0.65


## Intended uses & limitations

Feed a corpus of tweets to the model to generate label if input is indicative of depression or not. 

Limitation: All token sequences longer than 512 are automatically truncated.

## Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3.39e-05
- train_batch_size: 16
- eval_batch_size: 16
- weight_decay: 0.13
- num_epochs: 3.0

## Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.68          | 1.0   | 625  | 0.1385          | 0.9745   |
| 0.60          | 2.0   | 1250 | 0.1385          | 0.9745   |
| 0.52          | 3.0   | 1875 | 0.1385          | 0.9745   |

| Epoch | Training Loss | Validation Loss | Accuracy |    F1    | Precision |  Recall  |    AUC   |
|:-----:|:-------------:|:---------------:|:--------:|:--------:|:---------:|:--------:|:--------:|
| 1.0   | 0.68          | 0.66            | 0.59     | 0.63     | 0.56      | 0.73     | 0.59     |
| 2.0   | 0.60          | 0.68            | 0.63     | 0.69     | 0.59      | 0.83     | 0.63     |
| 3.0   | 0.52          | 0.67            | 0.64     | 0.66     | 0.62      | 0.72     | 0.65     |