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| This guide shows specific methods for processing text datasets. Learn how to: |
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| - Tokenize a dataset with [`~Dataset.map`]. |
| - Align dataset labels with label ids for NLI datasets. |
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| For a guide on how to process any type of dataset, take a look at the <a class="underline decoration-sky-400 decoration-2 font-semibold" href="./process">general process guide</a>. |
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| The [`~Dataset.map`] function supports processing batches of examples at once which speeds up tokenization. |
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| Load a tokenizer from 🤗 [Transformers](https://huggingface.co/transformers/): |
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| ```py |
| >>> from transformers import AutoTokenizer |
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| >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") |
| ``` |
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| Set the `batched` parameter to `True` in the [`~Dataset.map`] function to apply the tokenizer to batches of examples: |
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| ```py |
| >>> dataset = dataset.map(lambda examples: tokenizer(examples["text"]), batched=True) |
| >>> dataset[0] |
| {'text': 'the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .', |
| 'label': 1, |
| 'input_ids': [101, 1996, 2600, 2003, 16036, 2000, 2022, 1996, 7398, 2301, 1005, 1055, 2047, 1000, 16608, 1000, 1998, 2008, 2002, 1005, 1055, 2183, 2000, 2191, 1037, 17624, 2130, 3618, 2084, 7779, 29058, 8625, 13327, 1010, 3744, 1011, 18856, 19513, 3158, 5477, 4168, 2030, 7112, 16562, 2140, 1012, 102], |
| 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], |
| 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]} |
| ``` |
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| The [`~Dataset.map`] function converts the returned values to a PyArrow-supported format. But explicitly returning the tensors as NumPy arrays is faster because it is a natively supported PyArrow format. Set `return_tensors="np"` when you tokenize your text: |
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| ```py |
| >>> dataset = dataset.map(lambda examples: tokenizer(examples["text"], return_tensors="np"), batched=True) |
| ``` |
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| The [`~Dataset.align_labels_with_mapping`] function aligns a dataset label id with the label name. Not all 🤗 Transformers models follow the prescribed label mapping of the original dataset, especially for NLI datasets. For example, the [MNLI](https://huggingface.co/datasets/glue) dataset uses the following label mapping: |
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| ```py |
| >>> label2id = {"entailment": 0, "neutral": 1, "contradiction": 2} |
| ``` |
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| To align the dataset label mapping with the mapping used by a model, create a dictionary of the label name and id to align on: |
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| ```py |
| >>> label2id = {"contradiction": 0, "neutral": 1, "entailment": 2} |
| ``` |
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| Pass the dictionary of the label mappings to the [`~Dataset.align_labels_with_mapping`] function, and the column to align on: |
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| ```py |
| >>> from datasets import load_dataset |
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| >>> mnli = load_dataset("glue", "mnli", split="train") |
| >>> mnli_aligned = mnli.align_labels_with_mapping(label2id, "label") |
| ``` |
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| You can also use this function to assign a custom mapping of labels to ids. |
