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---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: fine-tuning-xlmr-large
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. -->
# fine-tuning-xlmr-large
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7558
- Accuracy: 0.7692
- Precision: 0.7692
- Recall: 0.7692
- F1 Score: 0.7693
## 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-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 101
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 1.3385 | 1.0 | 10330 | 1.8072 | 0.5708 | 0.5708 | 0.5708 | 0.5622 |
| 1.7231 | 2.0 | 20660 | 1.8354 | 0.6445 | 0.6445 | 0.6445 | 0.6454 |
| 1.4049 | 3.0 | 30990 | 1.8380 | 0.6969 | 0.6969 | 0.6969 | 0.6990 |
| 1.4543 | 4.0 | 41320 | 1.5726 | 0.7415 | 0.7415 | 0.7415 | 0.7417 |
| 1.4139 | 5.0 | 51650 | 1.6838 | 0.7424 | 0.7424 | 0.7424 | 0.7439 |
| 1.2368 | 6.0 | 61980 | 1.6794 | 0.7424 | 0.7424 | 0.7424 | 0.7448 |
| 1.0418 | 7.0 | 72310 | 1.6720 | 0.7542 | 0.7542 | 0.7542 | 0.7556 |
| 1.246 | 8.0 | 82640 | 1.6746 | 0.7638 | 0.7638 | 0.7638 | 0.7642 |
| 0.9896 | 9.0 | 92970 | 1.7497 | 0.7674 | 0.7674 | 0.7674 | 0.7666 |
| 0.9855 | 10.0 | 103300 | 1.7558 | 0.7692 | 0.7692 | 0.7692 | 0.7693 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
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