metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-bert-text-classification
results: []
finetuning-bert-text-classification
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2394
- Accuracy: 0.9073
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2612 | 1.0 | 6250 | 0.2394 | 0.9073 |
| 0.1966 | 2.0 | 12500 | 0.2488 | 0.9184 |
| 0.1737 | 3.0 | 18750 | 0.2759 | 0.9192 |
| 0.1415 | 4.0 | 25000 | 0.3322 | 0.9165 |
| 0.0857 | 5.0 | 31250 | 0.3835 | 0.9199 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1