Update README.md

README.md

@@ -38,6 +38,11 @@ More information needed
 ## Training procedure
 
 - Preprocessing function was created to tokenize the text and truncate the sequences longer than DistilBERT max seq length. Datasets [map](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.map) function was used to apply the preprocessing func over the entire dataset.
+- DataCollatorWithPadding is more efficiently method to dynamically pad the sequences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length.
+- To evaluate model performance during training, it's quite helpful to include a metric. Load the [accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) metric from [Evaluate](https://huggingface.co/docs/evaluate/index) library.
+- Define training hyperparameters in [TrainingArguments](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.TrainingArguments). To push the model to the hub, we need to set `push_to_hub=True`. At the end of each epoch, the [Trainer](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.Trainer) will evaluate the accuracy and save the training checkpoint.
+- Pass the trainingargs to the Trainer along with the model, dataset, tokenizer, data_collator.
+- Call [train()](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.Trainer.train) to finetune your model.
 ### Training hyperparameters
 
 The following hyperparameters were used during training: