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---
license: cc-by-nc-4.0
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-base-azerbaijani
results: []
datasets:
- hajili/azerbaijani-various-corpus
language:
- az
metrics:
- perplexity
---
<!-- 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. -->
This model is a continued pre-trained version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on a various cleaned community corpus.
It achieves the following results on the evaluation set:
- Loss: 1.1697
We thank Microsoft Accelerating Foundation Models Research Program for supporting our research.
Authors: Mammad Hajili, Duygu Ataman
## Model description
The model was trained on masked language model task on a single V100 GPU for 68 hours. For downstream tasks, it requires to be fine-tuned based on objective of the task.
## Training and evaluation data
The training data is clean mix of various Azerbaijani corpus shared by the community.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-------:|:---------------:|
| 1.6126 | 0.2500 | 100910 | 1.4818 |
| 1.4961 | 0.5000 | 201820 | 1.4163 |
| 1.4324 | 0.7500 | 302730 | 1.3371 |
| 1.387 | 1.0000 | 403640 | 1.3070 |
| 1.3488 | 1.2500 | 504550 | 1.2649 |
| 1.323 | 1.5000 | 605460 | 1.2581 |
| 1.3006 | 1.7500 | 706370 | 1.2066 |
| 1.2866 | 2.0000 | 807280 | 1.2095 |
| 1.2646 | 2.2500 | 908190 | 1.2019 |
| 1.2492 | 2.5000 | 1009100 | 1.1779 |
| 1.2425 | 2.7500 | 1110010 | 1.1742 |
- Validation loss at epoch 3: 1.1697
- Perplexity at epoch 3: 3.22
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 |