---
license: cc-by-nc-4.0
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-base-azerbaijani-wwm
  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 an various cleaned community corpus.
It achieves the following results on the evaluation set:
- Loss: 2.8039

We thank Microsoft Accelerating Foundation Models Research Program for supporting our research.
Authors: Mammad Hajili, Duygu Ataman

## Model description

The model was trained on whole word masked language model task on a single V100 GPU for 55 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 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 |
|:-------------:|:------:|:-------:|:---------------:|
| 3.4315        | 0.2500 | 100910  | 3.3178          |
| 3.2537        | 0.5000 | 201820  | 3.1369          |
| 3.1598        | 0.7500 | 302730  | 3.0042          |
| 3.0927        | 1.0000 | 403640  | 2.9691          |
| 3.0353        | 1.2500 | 504550  | 2.9385          |
| 2.9947        | 1.5000 | 605460  | 2.9062          |
| 2.9586        | 1.7500 | 706370  | 2.8547          |
| 2.9389        | 2.0000 | 807280  | 2.7979          |
| 2.9071        | 2.2500 | 908190  | 2.8124          |
| 2.8871        | 2.5000 | 1009100 | 2.7924          |
| 2.8792        | 2.7500 | 1110010 | 2.7697          |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1