---
license: mit
base_model: prajjwal1/bert-tiny
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: Server-S04
  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. -->

# Server-S04

This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co./prajjwal1/bert-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6591
- Accuracy: 0.62
- F1: 0.6205

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.01  | 50   | 0.6891          | 0.54     | 0.3787 |
| No log        | 0.01  | 100  | 0.6893          | 0.54     | 0.3787 |
| No log        | 0.02  | 150  | 0.6876          | 0.54     | 0.3787 |
| No log        | 0.03  | 200  | 0.6978          | 0.48     | 0.4231 |
| No log        | 0.03  | 250  | 0.6899          | 0.5      | 0.4878 |
| No log        | 0.04  | 300  | 0.6825          | 0.57     | 0.5577 |
| No log        | 0.04  | 350  | 0.6782          | 0.62     | 0.6205 |
| No log        | 0.05  | 400  | 0.6692          | 0.6      | 0.5981 |
| No log        | 0.06  | 450  | 0.6688          | 0.58     | 0.5664 |
| 0.6773        | 0.06  | 500  | 0.6692          | 0.6      | 0.5966 |
| 0.6773        | 0.07  | 550  | 0.6642          | 0.62     | 0.62   |
| 0.6773        | 0.08  | 600  | 0.6577          | 0.65     | 0.6505 |
| 0.6773        | 0.08  | 650  | 0.6618          | 0.6      | 0.5992 |
| 0.6773        | 0.09  | 700  | 0.6617          | 0.62     | 0.62   |
| 0.6773        | 0.09  | 750  | 0.6641          | 0.62     | 0.6205 |
| 0.6773        | 0.1   | 800  | 0.6573          | 0.62     | 0.62   |
| 0.6773        | 0.11  | 850  | 0.6625          | 0.61     | 0.6096 |
| 0.6773        | 0.11  | 900  | 0.6625          | 0.63     | 0.6303 |
| 0.6773        | 0.12  | 950  | 0.6632          | 0.62     | 0.6181 |
| 0.6414        | 0.13  | 1000 | 0.6613          | 0.62     | 0.6206 |
| 0.6414        | 0.13  | 1050 | 0.6594          | 0.62     | 0.6206 |
| 0.6414        | 0.14  | 1100 | 0.6607          | 0.62     | 0.6206 |
| 0.6414        | 0.14  | 1150 | 0.6580          | 0.62     | 0.6205 |
| 0.6414        | 0.15  | 1200 | 0.6628          | 0.62     | 0.6205 |
| 0.6414        | 0.16  | 1250 | 0.6591          | 0.62     | 0.6205 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0