I'm trying to tokenize the squad dataset following the huggingface tutorial:
from datasets import load_dataset
from transformers import RobertaTokenizer
from transformers import logging
logging.set_verbosity_error()
dataset = load_dataset('squad')
checkpoint = 'roberta-base'
tokenizer = RobertaTokenizer.from_pretrained(checkpoint)
def tokenize_function(example):
return tokenizer(example['question'], example['context'], [d['text'][0] for d in example['answers']], truncation=True)
tokenized_datasets = dataset['train'].map(tokenize_function, batched=True)
But when I print
tokenized_datasets
I get
Dataset({
features: ['id', 'title', 'context', 'question', 'answers', 'input_ids', 'attention_mask'],
num_rows: 87599
})
But shouldn't this return 3 input_ids, one for the question one for the context and one for the answer?
Is that line of code:
tokenizer(example['question'], example['context'], [d['text'][0] for d in example['answers']], truncation=True)
shown in the course?
A Tokenizer accepts plenty of parameters with its __call__ method (documentation). Since you have only specified truncation by its name, the other parameter values are determined by their position. That means, you are executing:
tokenizer(text=example['question'], text_pair=example['context'], add_special_tokens=[d['text'][0] for d in example['answers']], truncation=True)
After you execute your code the sample with the id 5733be284776f41900661182 becomes:
{'id': '5733be284776f41900661182',
'title': 'University_of_Notre_Dame',
'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',
'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
'answers': {'text': ['Saint Bernadette Soubirous'], 'answer_start': [515]},
'input_ids': [0, 3972, 2661, 222, 5, 9880, 2708, 2346, 2082, 11, 504, 4432, 11, 226, 2126, 10067, 1470, 116, 2, 2, 37848, 37471, 28108, 6, 5, 334, 34, 10, 4019, 2048, 4, 497, 1517, 5, 4326, 6919, 18, 1637, 31346, 16, 10, 9030, 9577, 9, 5, 9880, 2708, 4, 29261, 11, 760, 9, 5, 4326, 6919, 8, 2114, 24, 6, 16, 10, 7621, 9577, 9, 4845, 19, 3701, 62, 33161, 19, 5, 7875, 22, 39043, 1459, 1614, 1464, 13292, 4977, 845, 4130, 7, 5, 4326, 6919, 16, 5, 26429, 2426, 9, 5, 25095, 6924, 4, 29261, 639, 5, 32394, 2426, 16, 5, 7461, 26187, 6, 10, 19035, 317, 9, 9621, 8, 12456, 4, 85, 16, 10, 24633, 9, 5, 11491, 26187, 23, 226, 2126, 10067, 6, 1470, 147, 5, 9880, 2708, 2851, 13735, 352, 1382, 7, 6130, 6552, 625, 3398, 208, 22895, 853, 1827, 11, 504, 4432, 4, 497, 5, 253, 9, 5, 1049, 1305, 36, 463, 11, 10, 2228, 516, 14, 15230, 149, 155, 19638, 8, 5, 2610, 25336, 238, 16, 10, 2007, 6, 2297, 7326, 9577, 9, 2708, 4, 2],
'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, 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, 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, 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]}
The input_ids are the concatenation of text and text_pair:
tokenizer.decode([0, 3972, 2661, 222, 5, 9880, 2708, 2346, 2082, 11, 504, 4432, 11, 226, 2126, 10067, 1470, 116, 2, 2, 37848, 37471, 28108, 6, 5, 334, 34, 10, 4019, 2048, 4, 497, 1517, 5, 4326, 6919, 18, 1637, 31346, 16, 10, 9030, 9577, 9, 5, 9880, 2708, 4, 29261, 11, 760, 9, 5, 4326, 6919, 8, 2114, 24, 6, 16, 10, 7621, 9577, 9, 4845, 19, 3701, 62, 33161, 19, 5, 7875, 22, 39043, 1459, 1614, 1464, 13292, 4977, 845, 4130, 7, 5, 4326, 6919, 16, 5, 26429, 2426, 9, 5, 25095, 6924, 4, 29261, 639, 5, 32394, 2426, 16, 5, 7461, 26187, 6, 10, 19035, 317, 9, 9621, 8, 12456, 4, 85, 16, 10, 24633, 9, 5, 11491, 26187, 23, 226, 2126, 10067, 6, 1470, 147, 5, 9880, 2708, 2851, 13735, 352, 1382, 7, 6130, 6552, 625, 3398, 208, 22895, 853, 1827, 11, 504, 4432, 4, 497, 5, 253, 9, 5, 1049, 1305, 36, 463, 11, 10, 2228, 516, 14, 15230, 149, 155, 19638, 8, 5, 2610, 25336, 238, 16, 10, 2007, 6, 2297, 7326, 9577, 9, 2708, 4, 2])
Output:
<s>To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?</s></s>Architecturally, the school has a Catholic character. Atop the Main Building's gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.</s>
That is a common approach to handling extractive questions-answering tasks. In this, the answers are not seen as input but are only needed as a target (i.e. predicting start and end position).
Edit:
The OP specified the question in the comments and wants to know how the input_ids of the three text entities: question, context, and answer can be returned. All that needs to be changed is that the tokenize_function encodes the entities independently and returns a dict:
from datasets import load_dataset
from transformers import RobertaTokenizer
dataset = load_dataset('squad')
checkpoint = 'roberta-base'
tokenizer = RobertaTokenizer.from_pretrained(checkpoint)
def tokenize_function(example):
question_o = tokenizer(example['question'], truncation=True)
context_o = tokenizer(example['context'], truncation=True)
answer_o = tokenizer([d['text'][0] for d in example['answers']], truncation=True)
return {"question_input_ids": question_o.input_ids, "question_attention_mask": question_o.attention_mask, "context_input_ids": context_o.input_ids, "context_attention_mask": context_o.attention_mask, "answer_input_ids": answer_o.input_ids, "answer_attention_mask": answer_o.attention_mask}
tokenized_datasets = dataset['train'].map(tokenize_function, batched=True)