
# Persian_ChatBot_dataset_Fine_Tuning_Alpaca_Model
Persian ChatBot dataset, fine-tune LLaMa on instructed data (preprocessed alpaca dataset). [GitHub](https://github.com/AliEdalat/ChatBot_for_persian_LLaMA_fine_tune.git)

- we use [preprocessed alpaca dataset](https://github.com/thisserand/alpaca-lora-finetune-language.git) as a dataset. we translate no_translate data to persian with [mt5](https://huggingface.co/persiannlp/mt5-large-parsinlu-translation_en_fa). ([train dataset](https://huggingface.co/datasets/AliEdalat/Persian_ChatBot_dataset_Fine_Tuning_Alpaca_Model/tree/main) and [test data](https://huggingface.co/datasets/AliEdalat/Persian_ChatBot_dataset_Fine_Tuning_Alpaca_Model/tree/main) with 2k example is ready)
- we use LLaMA as a generative model for creating a chatbot model. we fine-tune the model with our Persian dataset and test it.
- for improving ChatBot performance, replace "برای اینکه این کار را بکنم" with ""
task_categories:text-generation task_categories:conversational size_categories:1K<n<10K language:fa license:apache-2.0 region:us
{"language": ["fa"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-generation", "conversational"]}
TAGS
#task_categories-text-generation #task_categories-conversational #size_categories-1K<n<10K #language-Persian #license-apache-2.0 #region-us 


# Persian_ChatBot_dataset_Fine_Tuning_Alpaca_Model
Persian ChatBot dataset, fine-tune LLaMa on instructed data (preprocessed alpaca dataset). GitHub

- we use preprocessed alpaca dataset as a dataset. we translate no_translate data to persian with mt5. (train dataset and test data with 2k example is ready)
- we use LLaMA as a generative model for creating a chatbot model. we fine-tune the model with our Persian dataset and test it.
- for improving ChatBot performance, replace "برای اینکه این کار را بکنم" with ""
# Persian_ChatBot_dataset_Fine_Tuning_Alpaca_Model
Persian ChatBot dataset, fine-tune LLaMa on instructed data (preprocessed alpaca dataset). GitHub

- we use preprocessed alpaca dataset as a dataset. we translate no_translate data to persian with mt5. (train dataset and test data with 2k example is ready)
- we use LLaMA as a generative model for creating a chatbot model. we fine-tune the model with our Persian dataset and test it.
- for improving ChatBot performance, replace "برای اینکه این کار را بکنم" with ""
TAGS
#task_categories-text-generation #task_categories-conversational #size_categories-1K<n<10K #language-Persian #license-apache-2.0 #region-us 
 # Persian_ChatBot_dataset_Fine_Tuning_Alpaca_Model
Persian ChatBot dataset, fine-tune LLaMa on instructed data (preprocessed alpaca dataset). GitHub

- we use preprocessed alpaca dataset as a dataset. we translate no_translate data to persian with mt5. (train dataset and test data with 2k example is ready)
- we use LLaMA as a generative model for creating a chatbot model. we fine-tune the model with our Persian dataset and test it.
- for improving ChatBot performance, replace "برای اینکه این کار را بکنم" with ""
passage: TAGS
#task_categories-text-generation #task_categories-conversational #size_categories-1K<n<10K #language-Persian #license-apache-2.0 #region-us 
# Persian_ChatBot_dataset_Fine_Tuning_Alpaca_Model
Persian ChatBot dataset, fine-tune LLaMa on instructed data (preprocessed alpaca dataset). GitHub

- we use preprocessed alpaca dataset as a dataset. we translate no_translate data to persian with mt5. (train dataset and test data with 2k example is ready)
- we use LLaMA as a generative model for creating a chatbot model. we fine-tune the model with our Persian dataset and test it.
- for improving ChatBot performance, replace "برای اینکه این کار را بکنم" with ""