---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: final_V1-roberta-text-classification-model
  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. -->

# final_V1-roberta-text-classification-model

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2641
- Accuracy: 0.9502
- F1: 0.8186
- Precision: 0.8164
- Recall: 0.8225

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.6615        | 0.11  | 50   | 1.5503          | 0.3199   | 0.1016 | 0.2599    | 0.1518 |
| 0.6244        | 0.22  | 100  | 0.7198          | 0.7692   | 0.4656 | 0.4593    | 0.4818 |
| 0.3344        | 0.33  | 150  | 0.4852          | 0.8893   | 0.6484 | 0.6264    | 0.6733 |
| 0.2596        | 0.44  | 200  | 0.5277          | 0.8805   | 0.6398 | 0.6124    | 0.6748 |
| 0.2173        | 0.55  | 250  | 0.4417          | 0.8849   | 0.6577 | 0.6421    | 0.6750 |
| 0.2393        | 0.66  | 300  | 0.5221          | 0.8707   | 0.6511 | 0.6361    | 0.6684 |
| 0.2229        | 0.76  | 350  | 0.4997          | 0.8928   | 0.6602 | 0.6410    | 0.6814 |
| 0.1482        | 0.87  | 400  | 0.5111          | 0.8983   | 0.6409 | 0.6131    | 0.6810 |
| 0.1831        | 0.98  | 450  | 0.4251          | 0.8827   | 0.6827 | 0.7149    | 0.6957 |
| 0.1882        | 1.09  | 500  | 0.4130          | 0.9043   | 0.6805 | 0.7998    | 0.6878 |
| 0.1182        | 1.2   | 550  | 0.4513          | 0.9076   | 0.6973 | 0.7703    | 0.7004 |
| 0.101         | 1.31  | 600  | 0.3402          | 0.9221   | 0.7040 | 0.8097    | 0.7036 |
| 0.0749        | 1.42  | 650  | 0.1566          | 0.9658   | 0.8229 | 0.8350    | 0.8122 |
| 0.1294        | 1.53  | 700  | 0.1586          | 0.9675   | 0.8336 | 0.8327    | 0.8346 |
| 0.046         | 1.64  | 750  | 0.2010          | 0.9604   | 0.8264 | 0.8211    | 0.8334 |
| 0.0833        | 1.75  | 800  | 0.1707          | 0.9647   | 0.8285 | 0.8244    | 0.8330 |
| 0.0759        | 1.86  | 850  | 0.1625          | 0.9664   | 0.8278 | 0.8285    | 0.8271 |
| 0.0459        | 1.97  | 900  | 0.1831          | 0.9620   | 0.8258 | 0.8200    | 0.8328 |
| 0.0726        | 2.07  | 950  | 0.1753          | 0.9625   | 0.8279 | 0.8287    | 0.8276 |
| 0.0369        | 2.18  | 1000 | 0.1871          | 0.9650   | 0.8300 | 0.8252    | 0.8362 |
| 0.0456        | 2.29  | 1050 | 0.1524          | 0.9683   | 0.8320 | 0.8278    | 0.8367 |
| 0.0371        | 2.4   | 1100 | 0.1857          | 0.9631   | 0.8280 | 0.8219    | 0.8353 |
| 0.0106        | 2.51  | 1150 | 0.1850          | 0.9661   | 0.8318 | 0.8274    | 0.8370 |
| 0.0173        | 2.62  | 1200 | 0.2055          | 0.9647   | 0.8310 | 0.8259    | 0.8374 |
| 0.036         | 2.73  | 1250 | 0.1699          | 0.9694   | 0.8311 | 0.8267    | 0.8358 |
| 0.0176        | 2.84  | 1300 | 0.1780          | 0.9691   | 0.8325 | 0.8274    | 0.8382 |
| 0.0444        | 2.95  | 1350 | 0.1918          | 0.9672   | 0.8319 | 0.8275    | 0.8371 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2