metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: BERT_FPB_finetuned
results: []
BERT_FPB_finetuned
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5238
- Accuracy: 0.8711
- F1: 0.8705
- Precision: 0.8706
- Recall: 0.8711
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.6376 | 1.0 | 218 | 0.4370 | 0.8144 | 0.8157 | 0.8177 | 0.8144 |
| 0.4168 | 2.0 | 436 | 0.4208 | 0.8376 | 0.8356 | 0.8358 | 0.8376 |
| 0.2808 | 3.0 | 654 | 0.4520 | 0.8608 | 0.8606 | 0.8609 | 0.8608 |
| 0.0538 | 4.0 | 872 | 0.5238 | 0.8711 | 0.8705 | 0.8706 | 0.8711 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1