---
license: cc-by-4.0
base_model: deepset/xlm-roberta-base-squad2
tags:
- generated_from_trainer
- xlm-roberta
model-index:
- name: xlm-roberta-base-squad-ft-qa-en-mt-to-uzn
  results: []
datasets:
- med-alex/qa_mt_en_to_uzn
language:
- uz
metrics:
- exact_match
- f1
library_name: transformers
pipeline_tag: question-answering
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# xlm-roberta-base-squad-ft-qa-en-mt-to-uzn

This model is a fine-tuned version of [deepset/xlm-roberta-base-squad2](https://huggingface.co./deepset/xlm-roberta-base-squad2) on the med-alex/qa_mt_en_to_uzn dataset.

## Model description

This model is one of many models created within the framework of a project to study the solution of a QA task for low-resource languages using the example of Kazakh and Uzbek. 

Please see the [description](https://github.com/med-alex/turkic_qa?tab=readme-ov-file#добро-пожаловать-на-студенческий-проект-посвященный-решению-задачи-qa-для-низкоресурсных-языков-на-примере-казахского-и-узбекского-языка) of the project, where there is a description of the solution and the results of the models in order to choose the best model for the Kazakh or Uzbek language.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 28
- eval_batch_size: 28
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 10.0

### Framework versions

- Transformers 4.40.1
- Pytorch 2.0.0+cu118
- Datasets 2.18.0
- Tokenizers 0.19.1