metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: HW2_finetuned_model
results: []
language: en
license: mit
HW2_finetuned_model
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1321
- Accuracy: 0.97
- F1: 0.9700
- Precision: 0.9717
- Recall: 0.97
Model description
This model wwas used for text classification of the dataset found at huggingface.co/datasets/mrob937/desdep_text_dataset
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: 0.0004
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.1276 | 1.0 | 120 | 0.2365 | 0.9458 | 0.9457 | 0.9511 | 0.9458 |
| 0.4081 | 2.0 | 240 | 0.2115 | 0.9583 | 0.9583 | 0.9615 | 0.9583 |
| 0.1085 | 3.0 | 360 | 0.1289 | 0.9708 | 0.9708 | 0.9724 | 0.9708 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0