Update README.md

README.md

@@ -3,9 +3,19 @@ license: mit
 base_model: FacebookAI/xlm-roberta-large
 tags:
 - generated_from_trainer
+- xlm-roberta
 model-index:
 - name: xlm-roberta-large-ft-qa-ru-mt-to-uzn
   results: []
+datasets:
+- med-alex/qa_mt_ru_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
@@ -13,19 +23,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # xlm-roberta-large-ft-qa-ru-mt-to-uzn
 
-This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the med-alex/qa_mt_ru_to_uzn dataset.
 
 ## Model description
 
-More information needed
+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. 
 
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
+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
 
@@ -41,13 +45,9 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_ratio: 0.2
 - num_epochs: 5.0
 
-### Training results
-
-
-
 ### Framework versions
 
 - Transformers 4.40.1
 - Pytorch 2.0.0+cu118
 - Datasets 2.18.0
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1