---
license: apache-2.0
base_model: google-bert/bert-large-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: cfd_model2
  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. -->

# cfd_model2

This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0066
- Precision: 0.9978
- Recall: 0.9987
- F1: 0.9982
- Accuracy: 0.9986

## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0493        | 0.19  | 100  | 0.0352          | 0.9890    | 0.9930 | 0.9910 | 0.9937   |
| 0.0103        | 0.39  | 200  | 0.0273          | 0.9886    | 0.9956 | 0.9921 | 0.9946   |
| 0.0011        | 0.58  | 300  | 0.0190          | 0.9921    | 0.9978 | 0.9950 | 0.9960   |
| 0.0247        | 0.77  | 400  | 0.0167          | 0.9978    | 0.9943 | 0.9960 | 0.9968   |
| 0.0003        | 0.97  | 500  | 0.0269          | 0.9926    | 0.9978 | 0.9952 | 0.9961   |
| 0.0036        | 1.16  | 600  | 0.0133          | 0.9960    | 0.9960 | 0.9960 | 0.9968   |
| 0.0008        | 1.35  | 700  | 0.0222          | 0.9926    | 0.9987 | 0.9956 | 0.9965   |
| 0.0003        | 1.55  | 800  | 0.0287          | 0.9895    | 0.9974 | 0.9934 | 0.9953   |
| 0.0005        | 1.74  | 900  | 0.0132          | 0.9934    | 0.9982 | 0.9958 | 0.9970   |
| 0.0024        | 1.93  | 1000 | 0.0123          | 0.9952    | 0.9982 | 0.9967 | 0.9977   |
| 0.0007        | 2.13  | 1100 | 0.0099          | 0.9969    | 0.9943 | 0.9956 | 0.9965   |
| 0.0005        | 2.32  | 1200 | 0.0087          | 0.9978    | 0.9965 | 0.9971 | 0.9977   |
| 0.001         | 2.51  | 1300 | 0.0055          | 0.9974    | 0.9991 | 0.9982 | 0.9986   |
| 0.0002        | 2.71  | 1400 | 0.0049          | 0.9974    | 0.9982 | 0.9978 | 0.9986   |
| 0.0004        | 2.9   | 1500 | 0.0065          | 0.9969    | 0.9982 | 0.9976 | 0.9984   |
| 0.0002        | 3.09  | 1600 | 0.0071          | 0.9969    | 0.9978 | 0.9974 | 0.9982   |
| 0.0001        | 3.29  | 1700 | 0.0077          | 0.9974    | 0.9978 | 0.9976 | 0.9984   |
| 0.0002        | 3.48  | 1800 | 0.0072          | 0.9974    | 0.9978 | 0.9976 | 0.9984   |
| 0.0005        | 3.68  | 1900 | 0.0072          | 0.9974    | 0.9978 | 0.9976 | 0.9984   |
| 0.0207        | 3.87  | 2000 | 0.0066          | 0.9978    | 0.9987 | 0.9982 | 0.9986   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0