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

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: 0.1600
- Precision: 0.8614
- Recall: 0.9121
- F1: 0.8860
- Accuracy: 0.9548

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 327  | 0.1504          | 0.8629    | 0.8675 | 0.8652 | 0.9512   |
| 0.2057        | 2.0   | 654  | 0.1462          | 0.8664    | 0.9034 | 0.8845 | 0.9548   |
| 0.2057        | 3.0   | 981  | 0.1600          | 0.8614    | 0.9121 | 0.8860 | 0.9548   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.0+cu118
- Datasets 2.19.1
- Tokenizers 0.15.2