---
license: apache-2.0
base_model: bert-large-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results_bert-large-uncased
  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. -->

# results_bert-large-uncased

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2128
- Accuracy: 0.9141
- Precision: 0.9182
- Recall: 0.9421
- F1: 0.9300

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6415        | 0.09  | 50   | 0.5315          | 0.7175   | 0.6981    | 0.9394 | 0.8010 |
| 0.4007        | 0.18  | 100  | 0.7702          | 0.7243   | 0.9892    | 0.5505 | 0.7074 |
| 0.5158        | 0.28  | 150  | 0.4075          | 0.8591   | 0.8904    | 0.8748 | 0.8825 |
| 0.3934        | 0.37  | 200  | 0.2809          | 0.8763   | 0.9354    | 0.8546 | 0.8932 |
| 0.2691        | 0.46  | 250  | 0.3406          | 0.8832   | 0.8837    | 0.9294 | 0.9060 |
| 0.2814        | 0.55  | 300  | 0.2582          | 0.8768   | 0.8512    | 0.9651 | 0.9046 |
| 0.2735        | 0.64  | 350  | 0.2715          | 0.8953   | 0.8708    | 0.9711 | 0.9182 |
| 0.2411        | 0.74  | 400  | 0.2389          | 0.9103   | 0.9242    | 0.9279 | 0.9260 |
| 0.2371        | 0.83  | 450  | 0.2081          | 0.9104   | 0.9212    | 0.9316 | 0.9264 |
| 0.1974        | 0.92  | 500  | 0.2128          | 0.9141   | 0.9182    | 0.9421 | 0.9300 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2