---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bert-base-uncased_roberta-base
  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-base-uncased_roberta-base

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4695
- Accuracy: 0.8705
- F1: 0.8700
- Precision: 0.8734
- Recall: 0.8705

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8895        | 1.0   | 91   | 0.8628          | 0.6147   | 0.5774 | 0.5987    | 0.6147 |
| 0.5526        | 2.0   | 182  | 0.5921          | 0.7722   | 0.7705 | 0.7856    | 0.7722 |
| 0.3669        | 3.0   | 273  | 0.4204          | 0.8346   | 0.8328 | 0.8359    | 0.8346 |
| 0.282         | 4.0   | 364  | 0.4526          | 0.8471   | 0.8475 | 0.8487    | 0.8471 |
| 0.1444        | 5.0   | 455  | 0.4695          | 0.8705   | 0.8700 | 0.8734    | 0.8705 |
| 0.1611        | 6.0   | 546  | 0.5552          | 0.8502   | 0.8503 | 0.8541    | 0.8502 |
| 0.0951        | 7.0   | 637  | 0.6573          | 0.8440   | 0.8430 | 0.8457    | 0.8440 |
| 0.1256        | 8.0   | 728  | 0.5882          | 0.8393   | 0.8411 | 0.8569    | 0.8393 |
| 0.1021        | 9.0   | 819  | 0.5695          | 0.8612   | 0.8614 | 0.8632    | 0.8612 |
| 0.0762        | 10.0  | 910  | 0.8848          | 0.8003   | 0.7958 | 0.8109    | 0.8003 |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1