---
base_model: Anwaarma/Merged-Server-praj
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: S02-PC
  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. -->

# S02-PC

This model is a fine-tuned version of [Anwaarma/Merged-Server-praj](https://huggingface.co/Anwaarma/Merged-Server-praj) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5986
- Accuracy: 0.78
- F1: 0.8764

## 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: 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.0   | 50   | 0.5686          | 0.6      | 0.5981 |
| No log        | 0.01  | 100  | 0.5715          | 0.62     | 0.6205 |
| No log        | 0.01  | 150  | 0.5591          | 0.64     | 0.6400 |
| No log        | 0.01  | 200  | 0.5670          | 0.63     | 0.6288 |
| No log        | 0.02  | 250  | 0.5568          | 0.61     | 0.6106 |
| No log        | 0.02  | 300  | 0.5761          | 0.64     | 0.6383 |
| No log        | 0.02  | 350  | 0.5515          | 0.61     | 0.6106 |
| No log        | 0.03  | 400  | 0.5567          | 0.61     | 0.6087 |
| No log        | 0.03  | 450  | 0.5590          | 0.62     | 0.6192 |
| 0.606         | 0.03  | 500  | 0.5454          | 0.64     | 0.6404 |
| 0.606         | 0.04  | 550  | 0.5509          | 0.63     | 0.6303 |
| 0.606         | 0.04  | 600  | 0.5451          | 0.64     | 0.6393 |
| 0.606         | 0.04  | 650  | 0.5461          | 0.65     | 0.6488 |
| 0.606         | 0.05  | 700  | 0.5443          | 0.62     | 0.6192 |
| 0.606         | 0.05  | 750  | 0.5461          | 0.66     | 0.6593 |
| 0.606         | 0.05  | 800  | 0.5420          | 0.66     | 0.6604 |
| 0.606         | 0.06  | 850  | 0.5414          | 0.65     | 0.6502 |
| 0.606         | 0.06  | 900  | 0.5411          | 0.65     | 0.6505 |
| 0.606         | 0.06  | 950  | 0.5413          | 0.69     | 0.6834 |
| 0.584         | 0.07  | 1000 | 0.5432          | 0.64     | 0.6353 |
| 0.584         | 0.07  | 1050 | 0.5335          | 0.64     | 0.6383 |
| 0.584         | 0.07  | 1100 | 0.5483          | 0.67     | 0.6702 |
| 0.584         | 0.08  | 1150 | 0.5548          | 0.66     | 0.6605 |
| 0.584         | 0.08  | 1200 | 0.5590          | 0.63     | 0.6306 |
| 0.584         | 0.09  | 1250 | 0.5580          | 0.67     | 0.6697 |
| 0.584         | 0.09  | 1300 | 0.5616          | 0.65     | 0.6502 |
| 0.584         | 0.09  | 1350 | 0.5620          | 0.62     | 0.6131 |
| 0.584         | 0.1   | 1400 | 0.5509          | 0.61     | 0.6059 |
| 0.584         | 0.1   | 1450 | 0.5473          | 0.66     | 0.6605 |
| 0.573         | 0.1   | 1500 | 0.5497          | 0.66     | 0.6593 |
| 0.573         | 0.11  | 1550 | 0.5450          | 0.65     | 0.6502 |
| 0.573         | 0.11  | 1600 | 0.5484          | 0.67     | 0.6689 |
| 0.573         | 0.11  | 1650 | 0.5398          | 0.66     | 0.6584 |
| 0.573         | 0.12  | 1700 | 0.5350          | 0.65     | 0.6477 |
| 0.573         | 0.12  | 1750 | 0.5333          | 0.64     | 0.6370 |
| 0.573         | 0.12  | 1800 | 0.5635          | 0.64     | 0.6400 |
| 0.573         | 0.13  | 1850 | 0.5742          | 0.63     | 0.6297 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0