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@@ -3,9 +3,19 @@ license: cc-by-4.0
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  base_model: deepset/xlm-roberta-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: xlm-roberta-base-squad-ft-qa-tr-mt-to-kaz
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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
@@ -13,19 +23,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # xlm-roberta-base-squad-ft-qa-tr-mt-to-kaz
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- This model is a fine-tuned version of [deepset/xlm-roberta-base-squad2](https://huggingface.co/deepset/xlm-roberta-base-squad2) on the None dataset.
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  ## Model description
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- More information needed
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- ## Intended uses & limitations
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-
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- More information needed
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-
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- ## Training and evaluation data
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-
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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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-
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-
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-
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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: deepset/xlm-roberta-base-squad2
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  tags:
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  - generated_from_trainer
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+ - xlm-roberta
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  model-index:
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  - name: xlm-roberta-base-squad-ft-qa-tr-mt-to-kaz
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  results: []
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+ datasets:
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+ - med-alex/qa_mt_tr_to_kaz
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+ language:
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+ - kk
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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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  # xlm-roberta-base-squad-ft-qa-tr-mt-to-kaz
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+ 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_tr_to_kaz dataset.
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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