Update README.md

README.md

@@ -8,7 +8,22 @@ In addition, we provide the three models on two architectures small and base. Fo
 
 
 # How to use AraT5 models
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1GFOGolWPIfDvYdSNdGFrOXwu3Gu28k2b?usp=sharing)This is an example for fine-tuning **AraT5-base** for News Title Generation on the Aranews dataset 
+Below is an example for fine-tuning **AraT5-base** for News Title Generation on the Aranews dataset 
+``` bash
+!python run_trainier_seq2seq_huggingface.py \
+        --learning_rate 5e-5 \
+        --max_target_length 128 --max_source_length 128 \
+        --per_device_train_batch_size 8 --per_device_eval_batch_size 8 \
+        --model_name_or_path "UBC-NLP/AraT5-base" \
+        --output_dir "/content/AraT5_FT_title_generation" --overwrite_output_dir \
+        --num_train_epochs 3 \
+        --train_file "/content/ARGEn_title_genration_sample_train.tsv" \
+        --validation_file "/content/ARGEn_title_genration_sample_valid.tsv" \
+        --task "title_generation" --text_column "document" --summary_column "title" \
+        --load_best_model_at_end --metric_for_best_model "eval_bleu" --greater_is_better True --evaluation_strategy epoch --logging_strategy epoch --predict_with_generate\
+        --do_train --do_eval
+```
+For more details about the fine-tuning example, please read this notebook [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/UBC-NLP/araT5/blob/main/examples/Fine_tuning_AraT5.ipynb) 
 
 In addition, we release the fine-tuned checkpoint of the News Title Generation (NGT) which is described in the paper. The model available at Huggingface ([UBC-NLP/AraT5-base-title-generation](https://huggingface.co/UBC-NLP/AraT5-base-title-generation)).