---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm_r_base-finetuned_after_mrp-v2-lucky-cloud-13
  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-lucky-cloud-13

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: 0.3887
- Precision 0: 0.8520
- Precision 1: 0.8061
- Recall 0: 0.8721
- Recall 1: 0.7783
- F1 0: 0.8619
- F1 1: 0.7920
- Precision Weighted: 0.8334
- Recall Weighted: 0.834
- F1 Weighted: 0.8335
- F1 Macro: 0.8269

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-----------:|:--------:|:--------:|:------:|:------:|:------------------:|:---------------:|:-----------:|:--------:|
| 0.521         | 1.0   | 469  | 0.4041          | 0.8088      | 0.8493      | 0.9172   | 0.6828   | 0.8596 | 0.7570 | 0.8252             | 0.822           | 0.8179      | 0.8083   |
| 0.3826        | 2.0   | 938  | 0.3844          | 0.8719      | 0.7672      | 0.8296   | 0.8217   | 0.8502 | 0.7935 | 0.8294             | 0.8264          | 0.8272      | 0.8219   |
| 0.3552        | 3.0   | 1407 | 0.3887          | 0.8520      | 0.8061      | 0.8721   | 0.7783   | 0.8619 | 0.7920 | 0.8334             | 0.834           | 0.8335      | 0.8269   |
| 0.2801        | 4.0   | 1876 | 0.4242          | 0.8648      | 0.7843      | 0.8485   | 0.8059   | 0.8566 | 0.7949 | 0.8321             | 0.8312          | 0.8315      | 0.8258   |
| 0.2223        | 5.0   | 2345 | 0.4754          | 0.8755      | 0.7726      | 0.8337   | 0.8266   | 0.8541 | 0.7987 | 0.8337             | 0.8308          | 0.8316      | 0.8264   |


### Framework versions

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