metadata
library_name: transformers
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased
results: []
datasets:
- surrey-nlp/PLOD-CW-25
bert-base-uncased
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3109
- Precision: 0.7725
- Recall: 0.8635
- F1: 0.8155
- Accuracy: 0.8922
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: Use OptimizerNames.ADAMW_TORCH 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 125 | 0.3279 | 0.7562 | 0.8527 | 0.8016 | 0.8860 |
| No log | 2.0 | 250 | 0.3262 | 0.7634 | 0.8642 | 0.8107 | 0.8901 |
| No log | 3.0 | 375 | 0.3109 | 0.7725 | 0.8635 | 0.8155 | 0.8922 |
Framework versions
- Transformers 4.51.1
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1