metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-tweet-disaster-classification
results: []
bert-base-uncased-tweet-disaster-classification
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5396
- Accuracy: 0.8076
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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 96 | 0.6598 | 0.7439 |
| No log | 2.0 | 192 | 0.4624 | 0.8011 |
| No log | 3.0 | 288 | 0.4350 | 0.8148 |
| No log | 4.0 | 384 | 0.4326 | 0.8188 |
| No log | 5.0 | 480 | 0.4331 | 0.8247 |
| 0.4631 | 6.0 | 576 | 0.4566 | 0.8227 |
| 0.4631 | 7.0 | 672 | 0.4711 | 0.8194 |
| 0.4631 | 8.0 | 768 | 0.5045 | 0.8102 |
| 0.4631 | 9.0 | 864 | 0.5400 | 0.8050 |
| 0.4631 | 10.0 | 960 | 0.5396 | 0.8076 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0