---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: 20240320103638_slow_musk
  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. -->

# 20240320103638_slow_musk

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0264
- Precision: 0.9792
- Recall: 0.9784
- F1: 0.9788
- Accuracy: 0.9892

## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 69
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 350
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0749        | 0.09  | 300  | 0.0578          | 0.9604    | 0.9502 | 0.9553 | 0.9771   |
| 0.0837        | 0.17  | 600  | 0.0642          | 0.9513    | 0.9529 | 0.9521 | 0.9751   |
| 0.0764        | 0.26  | 900  | 0.0592          | 0.9553    | 0.9566 | 0.9559 | 0.9771   |
| 0.0667        | 0.34  | 1200 | 0.0524          | 0.9614    | 0.9606 | 0.9610 | 0.9796   |
| 0.0603        | 0.43  | 1500 | 0.0477          | 0.9634    | 0.9641 | 0.9637 | 0.9811   |
| 0.0542        | 0.51  | 1800 | 0.0422          | 0.9686    | 0.9674 | 0.9680 | 0.9836   |
| 0.048         | 0.6   | 2100 | 0.0379          | 0.9703    | 0.9708 | 0.9705 | 0.9851   |
| 0.0433        | 0.68  | 2400 | 0.0343          | 0.9728    | 0.9749 | 0.9738 | 0.9864   |
| 0.0387        | 0.77  | 2700 | 0.0316          | 0.9751    | 0.9749 | 0.9750 | 0.9872   |
| 0.035         | 0.85  | 3000 | 0.0288          | 0.9766    | 0.9781 | 0.9773 | 0.9883   |
| 0.0328        | 0.94  | 3300 | 0.0264          | 0.9792    | 0.9784 | 0.9788 | 0.9892   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.0a0+6a974be
- Datasets 2.18.0
- Tokenizers 0.15.2