---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- liar
metrics:
- accuracy
model-index:
- name: liar_binaryclassifier_roberta_base
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: liar
      type: liar
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5770065075921909
---

<!-- 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. -->

# liar_binaryclassifier_roberta_base

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the liar dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6621
- Model Preparation Time: 0.0069
- Accuracy: 0.5770

## 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: 3e-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 | Model Preparation Time | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:--------:|
| 0.6934        | 1.0   | 461  | 0.6843          | 0.0069                 | 0.5553   |
| 0.6859        | 2.0   | 922  | 0.6815          | 0.0069                 | 0.5531   |
| 0.6774        | 3.0   | 1383 | 0.6666          | 0.0069                 | 0.5597   |
| 0.6671        | 4.0   | 1844 | 0.6742          | 0.0069                 | 0.5748   |
| 0.6596        | 5.0   | 2305 | 0.6621          | 0.0069                 | 0.5770   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1