---
library_name: transformers
license: apache-2.0
base_model: bert-large-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bert-large-uncased_fold_0
  results: []
---

<!-- 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-large-uncased_fold_0

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0362
- Accuracy: 0.9938
- F1: 0.9885
- Precision: 0.9967
- Recall: 0.9805

## 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: 20
- eval_batch_size: 20
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.063         | 1.0   | 7567  | 0.0503          | 0.9908   | 0.9827 | 0.9980    | 0.9680 |
| 0.0004        | 2.0   | 15134 | 0.0351          | 0.9938   | 0.9884 | 0.9942    | 0.9827 |
| 0.0122        | 3.0   | 22701 | 0.0362          | 0.9938   | 0.9885 | 0.9967    | 0.9805 |


### Framework versions

- Transformers 4.57.6
- Pytorch 2.11.0+cu128
- Datasets 4.8.4
- Tokenizers 0.22.2