---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-finetuned-partypredictor
  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. -->

# xlm-roberta-base-finetuned-partypredictor

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6783
- Accuracy: 0.2495

## 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: 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: 16

### Training results

| Training Loss | Epoch | Step  | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 1.7766        | 0.76  | 5000  | 0.1331   | 1.8909          |
| 1.7572        | 1.52  | 10000 | 0.1331   | 1.7809          |
| 1.7543        | 2.28  | 15000 | 0.1031   | 1.8126          |
| 1.7273        | 3.05  | 20000 | 0.1331   | 1.8048          |
| 1.7435        | 3.81  | 25000 | 0.2675   | 1.7892          |
| 1.7606        | 4.99  | 30000 | 0.3121   | 1.7848          |
| 1.7546        | 5.82  | 35000 | 0.3121   | 1.7737          |
| 1.7417        | 6.65  | 40000 | 0.3121   | 1.7699          |
| 1.7007        | 7.48  | 45000 | 0.1529   | 1.7088          |
| 1.7542        | 7.87  | 50000 | 0.1331   | 1.8058          |
| 1.75          | 8.66  | 55000 | 0.1331   | 1.8347          |
| 1.7505        | 10.05 | 60000 | 1.8079   | 0.1231          |
| 1.7545        | 10.88 | 65000 | 1.7756   | 0.3121          |
| 1.7322        | 11.72 | 70000 | 1.7371   | 0.2707          |
| 1.7082        | 12.56 | 75000 | 1.6886   | 0.2419          |
| 1.7035        | 13.4  | 80000 | 1.6844   | 0.2638          |
| 1.6889        | 14.23 | 85000 | 1.6728   | 0.2525          |
| 1.6779        | 15.07 | 90000 | 1.6737   | 0.2490          |
| 1.6821        | 15.91 | 95000 | 1.6783   | 0.2495          |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2