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--- |
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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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- xlm-roberta |
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model-index: |
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- name: xlm-roberta-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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should probably proofread and complete it, then remove this comment. --> |
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# xlm-roberta-base-squad-ft-qa-en-mt-to-uzn |
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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_en_to_uzn 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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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 28 |
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- eval_batch_size: 28 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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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 |
