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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-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 | |