---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm_r_base-finetuned_after_mrp-v2-lilac-water-12
  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-finetuned_after_mrp-v2-lilac-water-12

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: 0.4879
- Precision 0: 0.8632
- Precision 1: 0.8038
- Recall 0: 0.8667
- Recall 1: 0.7990
- F1 0: 0.8649
- F1 1: 0.8014
- Precision Weighted: 0.8391
- Recall Weighted: 0.8392
- F1 Weighted: 0.8391
- F1 Macro: 0.8332

## 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: 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: 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 | F1 Macro |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-----------:|:--------:|:--------:|:------:|:------:|:------------------:|:---------------:|:-----------:|:--------:|
| 0.5884        | 1.0   | 469  | 0.4450          | 0.8408      | 0.7812      | 0.8539   | 0.7635   | 0.8473 | 0.7723 | 0.8167             | 0.8172          | 0.8169      | 0.8098   |
| 0.3866        | 2.0   | 938  | 0.4053          | 0.8801      | 0.7518      | 0.8108   | 0.8384   | 0.8440 | 0.7927 | 0.8280             | 0.822           | 0.8232      | 0.8184   |
| 0.3641        | 3.0   | 1407 | 0.3683          | 0.8583      | 0.8091      | 0.8727   | 0.7892   | 0.8654 | 0.7990 | 0.8383             | 0.8388          | 0.8385      | 0.8322   |
| 0.282         | 4.0   | 1876 | 0.3963          | 0.8551      | 0.8096      | 0.8741   | 0.7833   | 0.8645 | 0.7962 | 0.8366             | 0.8372          | 0.8367      | 0.8303   |
| 0.2128        | 5.0   | 2345 | 0.4879          | 0.8632      | 0.8038      | 0.8667   | 0.7990   | 0.8649 | 0.8014 | 0.8391             | 0.8392          | 0.8391      | 0.8332   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1