---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: roberta-base-finetuned-OIG-mod-2
  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-base-finetuned-OIG-mod-2

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4275
- F1: 0.8487

## 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.7205        | 0.36  | 5000  | 0.6986          | 0.7007 |
| 0.6821        | 0.71  | 10000 | 0.6765          | 0.6998 |
| 0.6114        | 1.07  | 15000 | 0.6385          | 0.7281 |
| 0.5854        | 1.42  | 20000 | 0.6077          | 0.7404 |
| 0.5726        | 1.78  | 25000 | 0.5842          | 0.7572 |
| 0.4938        | 2.13  | 30000 | 0.5740          | 0.7722 |
| 0.4752        | 2.49  | 35000 | 0.5379          | 0.7847 |
| 0.473         | 2.84  | 40000 | 0.5139          | 0.7976 |
| 0.4042        | 3.2   | 45000 | 0.4977          | 0.8106 |
| 0.3909        | 3.55  | 50000 | 0.4783          | 0.8199 |
| 0.3779        | 3.91  | 55000 | 0.4507          | 0.8352 |
| 0.3341        | 4.26  | 60000 | 0.4542          | 0.8365 |
| 0.3202        | 4.62  | 65000 | 0.4333          | 0.8465 |
| 0.3101        | 4.97  | 70000 | 0.4275          | 0.8487 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2