---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
- recall
- accuracy
model-index:
- name: RoBERTa-THESIS
  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. -->

# RoBERTa-THESIS

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: 1.1698
- F1: 0.7701
- Recall: 0.7701
- Accuracy: 0.7701

## 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: 32
- eval_batch_size: 32
- 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 | F1     | Recall | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:--------:|
| 0.9918        | 1.0   | 1446  | 0.8174          | 0.7433 | 0.7433 | 0.7433   |
| 0.7223        | 2.0   | 2892  | 0.7799          | 0.7618 | 0.7618 | 0.7618   |
| 0.5389        | 3.0   | 4338  | 0.7730          | 0.7716 | 0.7716 | 0.7716   |
| 0.4073        | 4.0   | 5784  | 0.8121          | 0.7737 | 0.7737 | 0.7737   |
| 0.2985        | 5.0   | 7230  | 0.8841          | 0.7697 | 0.7697 | 0.7697   |
| 0.2233        | 6.0   | 8676  | 0.9573          | 0.7717 | 0.7717 | 0.7717   |
| 0.1679        | 7.0   | 10122 | 1.0132          | 0.7721 | 0.7721 | 0.7721   |
| 0.1233        | 8.0   | 11568 | 1.0948          | 0.7691 | 0.7691 | 0.7691   |
| 0.096         | 9.0   | 13014 | 1.1502          | 0.7689 | 0.7689 | 0.7689   |
| 0.0799        | 10.0  | 14460 | 1.1698          | 0.7701 | 0.7701 | 0.7701   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3