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README.md
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base_model: timpal0l/mdeberta-v3-base-squad2
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tags:
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- generated_from_trainer
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model-index:
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- name: mdeberta-base-squad-ft-qa-en-mt-to-uzn
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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## Model description
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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- lr_scheduler_warmup_ratio: 0.2
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- num_epochs: 10.0
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### Training results
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### Framework versions
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- Transformers 4.40.1
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- Pytorch 2.0.0+cu118
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- Datasets 2.18.0
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- Tokenizers 0.19.1
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base_model: timpal0l/mdeberta-v3-base-squad2
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tags:
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- generated_from_trainer
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- mdberta
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model-index:
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- name: mdeberta-base-squad-ft-qa-en-mt-to-uzn
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results: []
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datasets:
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- med-alex/qa_mt_en_to_uzn
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language:
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- uz
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metrics:
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- exact_match
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- f1
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library_name: transformers
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pipeline_tag: question-answering
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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## Model description
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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.
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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.
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## Training procedure
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- lr_scheduler_warmup_ratio: 0.2
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- num_epochs: 10.0
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### Framework versions
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- Transformers 4.40.1
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- Pytorch 2.0.0+cu118
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- Datasets 2.18.0
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- Tokenizers 0.19.1
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