---
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: roberta-large-detect-dep-v2
  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-large-detect-dep-v2

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7719
- Accuracy: 0.691
- F1: 0.7625

## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6278        | 1.0   | 751  | 0.5546          | 0.763    | 0.8227 |
| 0.5472        | 2.0   | 1502 | 0.5449          | 0.743    | 0.8160 |
| 0.4787        | 3.0   | 2253 | 0.5744          | 0.72     | 0.7929 |
| 0.423         | 4.0   | 3004 | 0.7290          | 0.702    | 0.7799 |
| 0.3803        | 5.0   | 3755 | 0.7719          | 0.691    | 0.7625 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3