---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: xlm-roberta-base-finetuned-marc-en
  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-marc-en

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: 0.6572
- Accuracy: 0.7805
- Recall: 0.6445
- Precision: 0.5522
- F1: 0.5948

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5098        | 1.0   | 309  | 0.4999          | 0.7498   | 0.0    | 0.0       | 0.0    |
| 0.4698        | 2.0   | 618  | 0.4456          | 0.7959   | 0.3456 | 0.6816    | 0.4586 |
| 0.3921        | 3.0   | 927  | 0.4620          | 0.8094   | 0.4561 | 0.6765    | 0.5448 |
| 0.3771        | 4.0   | 1236 | 0.4446          | 0.8172   | 0.5156 | 0.6766    | 0.5852 |
| 0.3454        | 5.0   | 1545 | 0.4567          | 0.8249   | 0.5609 | 0.6828    | 0.6159 |
| 0.2713        | 6.0   | 1854 | 0.4726          | 0.8136   | 0.6176 | 0.6301    | 0.6237 |
| 0.272         | 7.0   | 2163 | 0.5024          | 0.8108   | 0.6317 | 0.6194    | 0.6255 |
| 0.2478        | 8.0   | 2472 | 0.5689          | 0.8051   | 0.6516 | 0.6021    | 0.6259 |
| 0.1869        | 9.0   | 2781 | 0.6018          | 0.8044   | 0.7082 | 0.5910    | 0.6443 |
| 0.1575        | 10.0  | 3090 | 0.6700          | 0.8108   | 0.4986 | 0.6617    | 0.5687 |
| 0.1411        | 11.0  | 3399 | 0.7287          | 0.8157   | 0.5581 | 0.6545    | 0.6024 |
| 0.1014        | 12.0  | 3708 | 0.8177          | 0.8086   | 0.5269 | 0.6436    | 0.5794 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1