---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: XLM-R-BASE-VanillaFT-5E-spring-feather-1-D-08-03-T-08-15
  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-R-BASE-VanillaFT-5E-spring-feather-1-D-08-03-T-08-15

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4196
- Precision 0: 0.8318
- Precision 1: 0.7503
- Recall 0: 0.8119
- Recall 1: 0.7767
- F1 0: 0.8217
- F1 1: 0.7633
- Precision Weighted: 0.7987
- Recall Weighted: 0.7976
- F1 Weighted: 0.7980
- Accuracy: 0.7976
- F1 Macro: 0.7925

## 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: 16
- eval_batch_size: 16
- seed: 402
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision 0 | Precision 1 | Recall 0 | Recall 1 | F1 0   | F1 1   | Precision Weighted | Recall Weighted | F1 Weighted | Accuracy | F1 Macro |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-----------:|:--------:|:--------:|:------:|:------:|:------------------:|:---------------:|:-----------:|:--------:|:--------:|
| 0.5481        | 1.0   | 469  | 0.4437          | 0.7838      | 0.7441      | 0.8226   | 0.6900   | 0.8028 | 0.7161 | 0.7677             | 0.7688          | 0.7676      | 0.7688   | 0.7594   |
| 0.4178        | 2.0   | 938  | 0.4403          | 0.8529      | 0.6988      | 0.7519   | 0.8211   | 0.7994 | 0.7553 | 0.7904             | 0.7800          | 0.7815      | 0.7800   | 0.7773   |
| 0.3416        | 3.0   | 1407 | 0.4196          | 0.8318      | 0.7503      | 0.8119   | 0.7767   | 0.8217 | 0.7633 | 0.7987             | 0.7976          | 0.7980      | 0.7976   | 0.7925   |
| 0.2757        | 4.0   | 1876 | 0.4393          | 0.8430      | 0.7153      | 0.7735   | 0.8024   | 0.8068 | 0.7565 | 0.7912             | 0.7852          | 0.7864      | 0.7852   | 0.7816   |
| 0.2231        | 5.0   | 2345 | 0.4908          | 0.8343      | 0.7418      | 0.8031   | 0.7827   | 0.8184 | 0.7617 | 0.7968             | 0.7948          | 0.7954      | 0.7948   | 0.7901   |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1