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machine-translation-between-arabic-and-english-by-transformers's Introduction

Machine-Translation-between-Arabic-and-English-by-Transformers

Ways to apply fine tuning :

  • full fine tuning : train all internal model parameters (called full parameter tuning). While this option is simple (conceptually), it is the most computationally expensive. Additionally, a known issue with full parameter tuning is the phenomenon of catastrophic forgetting.

  • Transfer learning :The big idea with transfer learning (TL) is to preserve the useful representations/features the model has learned from past training when applying the model to a new task. This generally consists of dropping “the head” of a neural network (NN) and replacing it with a new one (e.g. adding new layers with randomized weights).

  • Parameter Efficient Fine tuning : PEFT involves augmenting a base model with a relatively small number of trainable parameters. The key result of this is a fine-tuning methodology that demonstrates comparable performance to full parameter tuning at a tiny fraction of the computational and storage cost.

  • LORA : PEFT encapsulates a family of techniques, one of which is the popular LoRA (Low-Rank Adaptation) method.

  • Model on Huggigface for ar_en translation: https://huggingface.co/Elalimy/my_awesome_peft_finetuned_helsinki_model

  • Model on Huggigface for ar_en translation: https://huggingface.co/Elalimy/finetuned_helsinki_peft_model__en_to_ar

  • Dataset used is custom dataset for computer science terminology in addition to puplic words it also on Huggingface: https://huggingface.co/datasets/Elalimy/Arabic_Eng_MT also used opus100 dataset it is be availabe on huggingface

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