# Dataset and Training Batching

**URL:** <https://discuss.huggingface.co/t/dataset-and-training-batching/13555>\
**Category:** Beginners\
**Created:** [January 10, 2022, 7:30pm UTC](https://discuss.huggingface.co/t/dataset-and-training-batching/13555 "2022-01-10T19:30:02Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![oqq09](https://avatars.discourse-cdn.com/v4/letter/o/f08c70/32.png) [@oqq09](https://discuss.huggingface.co/u/oqq09)\
**Post date:** [January 10, 2022, 7:30pm UTC](https://discuss.huggingface.co/t/dataset-and-training-batching/13555/1 "2022-01-10T19:30:02Z")

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Hello Everyone,

I have a question on batching, I am using the following to process datasets for fine-tuning:

```auto
test_ds = Dataset.from_pandas(test_df[['idx', 'question', 'sentence', 'label']])
  tokenizer_robert_base = AutoTokenizer.from_pretrained(robert_base)
  def preprocess_function_roberta_base(examples):
      return tokenizer_robert_base(examples[sentence1_key], examples[sentence2_key], max_length=max_input_length, truncation='only_second')
test_ds_en = test_ds.map(preprocess_function_roberta_base)

```

the .map() method has parameters `batched=True` and `batch_size=batch_size`. When and why would I use this? I ask this because I presume that batching in TrainingArguments below is where the batching should happen. What happens if I use both?

```auto
args = TrainingArguments(
      'checkpoints/',
      evaluation_strategy = "steps",
      learning_rate=learning_rate,
      per_device_train_batch_size=batch_size, per_device_eval_batch_size=batch_size,
      num_train_epochs=epochs,
      weight_decay=0.01,
      load_best_model_at_end=True,
      metric_for_best_model=metric_name, gradient_accumulation_steps=2)

```

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<div class="post-metadata">

**Author:** ![mariosasko](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/mariosasko/32/31548_2.png) [@mariosasko](https://discuss.huggingface.co/u/mariosasko)\
**Post date:** [February 9, 2022, 6:49pm UTC](https://discuss.huggingface.co/t/dataset-and-training-batching/13555/2 "2022-02-09T18:49:31Z")

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Hi!

With the `batched` flag in `map`, you control whether your map function will get a single example to process or a batch of samples, which size is determined by `batch_size` (`1000` by default), in a single call. It is advised to set `batched` to `True` whenever possible for better performance (e.g., our fast tokenizers can process a batch in parallel).

And Trainer’s `batch_size` controls the number of examples the model will get in one iteration for training/evaluation.