metadata
library_name: transformers
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Depression_binary
results: []
Depression_binary
This model is a fine-tuned version of roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5764
- Accuracy: 0.7696
- Precision: 0.7585
- Recall: 0.7716
- F1: 0.7650
- Auc: 0.7696
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auc |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 134 | 0.5236 | 0.7397 | 0.8056 | 0.6123 | 0.6957 | 0.7363 |
| No log | 2.0 | 268 | 0.5462 | 0.7565 | 0.7802 | 0.6948 | 0.7350 | 0.7548 |
| No log | 3.0 | 402 | 0.5764 | 0.7696 | 0.7585 | 0.7716 | 0.7650 | 0.7696 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1