---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Bert-RAdam-Large
  results: []
datasets:
- surrey-nlp/PLOD-CW-25
- surrey-nlp/PLODv2-filtered
---

<!-- 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-RAdam-Large

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on a subset of the PLODv2-filtered dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2110
- Precision: 0.7864
- Recall: 0.8598
- F1: 0.8215
- Accuracy: 0.9403

It achieves the following results on the test set:
- Loss: 0.1825
- Precision: 0.8017
- Recall: 0.8902
- F1: 0.8436
- Accuracy: 0.9500

## 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: 0.0001
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2244        | 1.0   | 500  | 0.1675          | 0.7653    | 0.8651 | 0.8121 | 0.9355   |
| 0.1231        | 2.0   | 1000 | 0.1673          | 0.7433    | 0.9011 | 0.8146 | 0.9375   |
| 0.0923        | 3.0   | 1500 | 0.1698          | 0.7867    | 0.8539 | 0.8189 | 0.9391   |
| 0.0657        | 4.0   | 2000 | 0.1865          | 0.7857    | 0.8405 | 0.8122 | 0.9394   |
| 0.0431        | 5.0   | 2500 | 0.2110          | 0.7864    | 0.8598 | 0.8215 | 0.9403   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1