metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: benchmark-finetuned-bert
results: []
benchmark-finetuned-bert
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3995
- Accuracy: 0.8479
- F1: 0.8480
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.7981 | 1.0 | 48 | 0.6349 | 0.7037 | 0.6527 |
| 0.5263 | 2.0 | 96 | 0.4732 | 0.8320 | 0.8321 |
| 0.3521 | 3.0 | 144 | 0.4009 | 0.8426 | 0.8413 |
| 0.268 | 4.0 | 192 | 0.3995 | 0.8479 | 0.8480 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1