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---
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
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