---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
pipeline_tag: text-classification
model-index:
- name: fator-fallacy-detector
  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. -->

# fator-fallacy-detector

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7968
- Accuracy: 0.8598
- F1 Macro: 0.6798
- F1 Weighted: 0.7825

## 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-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|
| 2.0353        | 1.0   | 69   | 2.2417          | 0.4041   | 0.3083   | 0.4288      |
| 1.1018        | 2.0   | 138  | 1.8271          | 0.5619   | 0.5319   | 0.5691      |
| 1.0166        | 3.0   | 207  | 1.0606          | 0.7808   | 0.6107   | 0.6679       |
| 0.7968        | 4.0   | 276  | 0.9268          | 0.8598   | 0.6798   | 0.7825      |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2