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

# our_data

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: 1.4250
- Precision: 0.4759
- Recall: 0.5476
- F1: 0.5092
- Accuracy: 0.7455

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.8353        | 0.4   | 500   | 1.6175          | 0.1212    | 0.1217 | 0.1215 | 0.5907   |
| 1.4071        | 0.81  | 1000  | 1.3137          | 0.2618    | 0.3228 | 0.2891 | 0.6518   |
| 1.1532        | 1.21  | 1500  | 1.2950          | 0.3154    | 0.3558 | 0.3344 | 0.6739   |
| 0.9969        | 1.61  | 2000  | 1.1882          | 0.3266    | 0.4034 | 0.3609 | 0.6783   |
| 0.922         | 2.01  | 2500  | 1.2653          | 0.3471    | 0.3995 | 0.3715 | 0.6873   |
| 0.739         | 2.42  | 3000  | 1.1592          | 0.3538    | 0.4339 | 0.3898 | 0.7034   |
| 0.6866        | 2.82  | 3500  | 1.2015          | 0.3521    | 0.4299 | 0.3871 | 0.7017   |
| 0.5554        | 3.22  | 4000  | 1.2555          | 0.4398    | 0.4643 | 0.4517 | 0.7329   |
| 0.5009        | 3.63  | 4500  | 1.2871          | 0.4098    | 0.4868 | 0.4450 | 0.7230   |
| 0.5117        | 4.03  | 5000  | 1.2482          | 0.4030    | 0.4974 | 0.4452 | 0.7279   |
| 0.3771        | 4.43  | 5500  | 1.3005          | 0.4300    | 0.4960 | 0.4607 | 0.7261   |
| 0.4357        | 4.83  | 6000  | 1.2412          | 0.4516    | 0.5251 | 0.4856 | 0.7395   |
| 0.3151        | 5.24  | 6500  | 1.3410          | 0.4423    | 0.5225 | 0.4791 | 0.7333   |
| 0.3219        | 5.64  | 7000  | 1.2903          | 0.425     | 0.5172 | 0.4666 | 0.7366   |
| 0.3405        | 6.04  | 7500  | 1.3366          | 0.4470    | 0.5304 | 0.4852 | 0.7471   |
| 0.2856        | 6.45  | 8000  | 1.3243          | 0.4415    | 0.5344 | 0.4835 | 0.7474   |
| 0.2723        | 6.85  | 8500  | 1.3962          | 0.4540    | 0.5291 | 0.4887 | 0.7398   |
| 0.2307        | 7.25  | 9000  | 1.4783          | 0.4671    | 0.5357 | 0.4991 | 0.7440   |
| 0.2484        | 7.66  | 9500  | 1.4250          | 0.4759    | 0.5476 | 0.5092 | 0.7455   |
| 0.2361        | 8.06  | 10000 | 1.4695          | 0.4700    | 0.5384 | 0.5018 | 0.7518   |
| 0.186         | 8.46  | 10500 | 1.5283          | 0.4587    | 0.5516 | 0.5009 | 0.7520   |
| 0.2188        | 8.86  | 11000 | 1.4357          | 0.4478    | 0.5450 | 0.4916 | 0.7471   |
| 0.2072        | 9.27  | 11500 | 1.4810          | 0.4770    | 0.5357 | 0.5047 | 0.7527   |
| 0.1817        | 9.67  | 12000 | 1.5041          | 0.4719    | 0.5450 | 0.5058 | 0.7532   |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0