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---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fedcsis_translated-slot_baseline-xlm_r-pl
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. -->
# fedcsis_translated-slot_baseline-xlm_r-pl
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the
[leyzer-fedcsis-translated](https://huggingface.co/datasets/cartesinus/leyzer-fedcsis-translated) dataset.
Results on untranslated test set:
- Precision: 0.5909
- Recall: 0.5766
- F1: 0.5836
- Accuracy: 0.7484
It achieves the following results on the evaluation set:
- Loss: 1.0761
- Precision: 0.7299
- Recall: 0.7427
- F1: 0.7363
- Accuracy: 0.8415
## 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
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.4842 | 1.0 | 814 | 0.7712 | 0.5858 | 0.6026 | 0.5941 | 0.7918 |
| 0.5128 | 2.0 | 1628 | 0.6435 | 0.6469 | 0.6828 | 0.6644 | 0.8119 |
| 0.3526 | 3.0 | 2442 | 0.7030 | 0.6823 | 0.7045 | 0.6933 | 0.8242 |
| 0.2142 | 4.0 | 3256 | 0.7695 | 0.7112 | 0.7243 | 0.7177 | 0.8381 |
| 0.1422 | 5.0 | 4070 | 0.8550 | 0.7203 | 0.7310 | 0.7256 | 0.8399 |
| 0.1188 | 6.0 | 4884 | 0.9209 | 0.7183 | 0.7333 | 0.7258 | 0.8391 |
| 0.0915 | 7.0 | 5698 | 0.9892 | 0.7238 | 0.7372 | 0.7305 | 0.8404 |
| 0.072 | 8.0 | 6512 | 1.0271 | 0.7230 | 0.7364 | 0.7296 | 0.8417 |
| 0.0626 | 9.0 | 7326 | 1.0608 | 0.7312 | 0.7417 | 0.7364 | 0.8419 |
| 0.0613 | 10.0 | 8140 | 1.0761 | 0.7299 | 0.7427 | 0.7363 | 0.8415 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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