---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: xlm_roberta_kriter
  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_kriter

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: 0.1944
- F1: 0.7931
- Roc Auc: 0.8977
- Accuracy: 0.7656

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|
| 0.3583        | 1.0   | 1151  | 0.3227          | 0.1209 | 0.5303  | 0.4766   |
| 0.289         | 2.0   | 2302  | 0.2266          | 0.6301 | 0.7660  | 0.6719   |
| 0.214         | 3.0   | 3453  | 0.1938          | 0.7500 | 0.8319  | 0.7344   |
| 0.1901        | 4.0   | 4604  | 0.1990          | 0.7328 | 0.8522  | 0.7188   |
| 0.1646        | 5.0   | 5755  | 0.1865          | 0.7664 | 0.8626  | 0.7344   |
| 0.1507        | 6.0   | 6906  | 0.1760          | 0.8030 | 0.8955  | 0.7773   |
| 0.1247        | 7.0   | 8057  | 0.1797          | 0.8010 | 0.9033  | 0.7695   |
| 0.1084        | 8.0   | 9208  | 0.1869          | 0.8051 | 0.8918  | 0.7812   |
| 0.0767        | 9.0   | 10359 | 0.1942          | 0.7931 | 0.8977  | 0.7617   |
| 0.0796        | 10.0  | 11510 | 0.1944          | 0.7931 | 0.8977  | 0.7656   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1