---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: 20240320102435_big_hinton
  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. -->

# 20240320102435_big_hinton

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: 0.0351
- Precision: 0.9436
- Recall: 0.9308
- F1: 0.9372
- Accuracy: 0.9859

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0805        | 0.09  | 300  | 0.0626          | 0.9020    | 0.8843 | 0.8931 | 0.9758   |
| 0.0969        | 0.18  | 600  | 0.0770          | 0.8912    | 0.8486 | 0.8694 | 0.9704   |
| 0.0879        | 0.27  | 900  | 0.0682          | 0.8943    | 0.8733 | 0.8837 | 0.9735   |
| 0.0778        | 0.36  | 1200 | 0.0612          | 0.9013    | 0.8891 | 0.8952 | 0.9762   |
| 0.0703        | 0.44  | 1500 | 0.0564          | 0.9137    | 0.8909 | 0.9021 | 0.9779   |
| 0.0638        | 0.53  | 1800 | 0.0521          | 0.9244    | 0.8975 | 0.9107 | 0.9799   |
| 0.0579        | 0.62  | 2100 | 0.0480          | 0.9309    | 0.9029 | 0.9167 | 0.9812   |
| 0.0534        | 0.71  | 2400 | 0.0447          | 0.9323    | 0.9095 | 0.9208 | 0.9825   |
| 0.049         | 0.8   | 2700 | 0.0399          | 0.9329    | 0.9236 | 0.9282 | 0.9841   |
| 0.0451        | 0.89  | 3000 | 0.0373          | 0.9411    | 0.9226 | 0.9318 | 0.9849   |
| 0.0424        | 0.98  | 3300 | 0.0351          | 0.9436    | 0.9308 | 0.9372 | 0.9859   |


### Framework versions

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