---
library_name: transformers
license: apache-2.0
base_model: google/electra-small-discriminator
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: electra-small-touche-rawplusctx-binary
  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. -->

# electra-small-touche-rawplusctx-binary

This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6499
- Accuracy: 0.64
- Macro F1: 0.6397
- Fallacy F1: 0.6505

## 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: 16
- 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Fallacy F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:----------:|
| 0.6922        | 1.0   | 47   | 0.6911          | 0.525    | 0.4203   | 0.1739     |
| 0.6858        | 2.0   | 94   | 0.6856          | 0.595    | 0.5927   | 0.6233     |
| 0.6565        | 3.0   | 141  | 0.6712          | 0.565    | 0.5496   | 0.4663     |
| 0.6361        | 4.0   | 188  | 0.6547          | 0.625    | 0.6200   | 0.6637     |
| 0.5926        | 5.0   | 235  | 0.6499          | 0.64     | 0.6397   | 0.6505     |


### Framework versions

- Transformers 5.9.0
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2