---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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