---
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-XL
  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-RAdam-XL

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1916
- Precision: 0.8057
- Recall: 0.8640
- F1: 0.8338
- Accuracy: 0.9455

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1843        | 1.0   | 1000 | 0.1410          | 0.7841    | 0.8672 | 0.8236 | 0.9436   |
| 0.1156        | 2.0   | 2000 | 0.1443          | 0.7981    | 0.8372 | 0.8172 | 0.9438   |
| 0.0862        | 3.0   | 3000 | 0.1562          | 0.7947    | 0.8961 | 0.8424 | 0.9477   |
| 0.0612        | 4.0   | 4000 | 0.1735          | 0.7976    | 0.8853 | 0.8392 | 0.9470   |
| 0.038         | 5.0   | 5000 | 0.1916          | 0.8057    | 0.8640 | 0.8338 | 0.9455   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1