---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bert_en_task-A
  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_en_task-A

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3077
- Accuracy: 0.9221
- Precision: 0.4610
- Recall: 0.5
- F1: 0.4797

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3612        | 1.0   | 90   | 0.2737          | 0.9221   | 0.4610    | 0.5    | 0.4797 |
| 0.3527        | 2.0   | 180  | 0.2659          | 0.9221   | 0.4610    | 0.5    | 0.4797 |
| 0.2968        | 3.0   | 270  | 0.3077          | 0.9221   | 0.4610    | 0.5    | 0.4797 |


### Framework versions

- Transformers 4.57.3
- Pytorch 2.9.1
- Datasets 4.4.1
- Tokenizers 0.22.1