---
license: mit
base_model: facebook/xlm-roberta-xl
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 5e-6_xlm-R-xl_Conspiracy_training_with_callbacks
  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. -->

# 5e-6_xlm-R-xl_Conspiracy_training_with_callbacks

This model is a fine-tuned version of [facebook/xlm-roberta-xl](https://huggingface.co/facebook/xlm-roberta-xl) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0624
- Macro F1: 0.9918
- Micro F1: 0.9919
- Accuracy: 0.9919

## 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: 5e-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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Macro F1 | Micro F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:--------:|
| 0.6122        | 1.0   | 502   | 0.2712          | 0.9549   | 0.9559   | 0.9559   |
| 0.1503        | 2.0   | 1004  | 0.1047          | 0.9741   | 0.9744   | 0.9744   |
| 0.0508        | 3.0   | 1506  | 0.0734          | 0.9835   | 0.9837   | 0.9837   |
| 0.0226        | 4.0   | 2008  | 0.0837          | 0.9811   | 0.9814   | 0.9814   |
| 0.014         | 5.0   | 2510  | 0.0562          | 0.9882   | 0.9884   | 0.9884   |
| 0.0014        | 6.0   | 3012  | 0.0514          | 0.9894   | 0.9895   | 0.9895   |
| 0.0016        | 7.0   | 3514  | 0.0501          | 0.9918   | 0.9919   | 0.9919   |
| 0.0002        | 8.0   | 4016  | 0.0554          | 0.9918   | 0.9919   | 0.9919   |
| 0.0001        | 9.0   | 4518  | 0.0607          | 0.9906   | 0.9907   | 0.9907   |
| 0.0001        | 10.0  | 5020  | 0.0856          | 0.9859   | 0.9861   | 0.9861   |
| 0.0143        | 11.0  | 5522  | 0.0377          | 0.9929   | 0.9930   | 0.9930   |
| 0.0001        | 12.0  | 6024  | 0.0538          | 0.9918   | 0.9919   | 0.9919   |
| 0.0001        | 13.0  | 6526  | 0.0568          | 0.9918   | 0.9919   | 0.9919   |
| 0.0012        | 14.0  | 7028  | 0.0582          | 0.9918   | 0.9919   | 0.9919   |
| 0.0           | 15.0  | 7530  | 0.0500          | 0.9929   | 0.9930   | 0.9930   |
| 0.0001        | 16.0  | 8032  | 0.0649          | 0.9918   | 0.9919   | 0.9919   |
| 0.0           | 17.0  | 8534  | 0.0649          | 0.9918   | 0.9919   | 0.9919   |
| 0.0           | 18.0  | 9036  | 0.0648          | 0.9918   | 0.9919   | 0.9919   |
| 0.0009        | 19.0  | 9538  | 0.0621          | 0.9918   | 0.9919   | 0.9919   |
| 0.0           | 20.0  | 10040 | 0.0624          | 0.9918   | 0.9919   | 0.9919   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1