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---
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 | |