---
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: baseline_roberta-large_epoch3_batch4_lr2e-05_w0.01
  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. -->

# baseline_roberta-large_epoch3_batch4_lr2e-05_w0.01

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: 0.5836
- Accuracy: 0.7164
- F1: 0.3943
- Precision: 0.9651
- Recall: 0.2478

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7073        | 1.0   | 788  | 0.6524          | 0.6274   | 0.0    | 0.0       | 0.0    |
| 0.668         | 2.0   | 1576 | 0.5835          | 0.6274   | 0.0    | 0.0       | 0.0    |
| 0.6148        | 3.0   | 2364 | 0.5836          | 0.7164   | 0.3943 | 0.9651    | 0.2478 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3