Merge multiple BatchEncoding or create tensorflow dataset from list of BatchEncoding objects-CodePudding

In a token labelling task I am using a transformers tokenizer, which outputs objects of the BatchEncoding class. I am tokenizing each text separately because I need to extract the labels from the text and re-arrange them after tokenizing (due to subtokens). However, I can't find a way to either create a tensorflow Dataset from the list of BatchEncoding objects or merge all the BatchEncoding objects into one to create the dataset.

Here are the main parts of the code:

tokenizer = BertTokenizerFast.from_pretrained('bert-base-multilingual-uncased')

def extract_labels(raw_text):
  # split text into words and extract label
  (...)
  return clean_words, labels


def tokenize_text(words, labels):

  # tokenize text
  tokens = tokenizer(words, is_split_into_words=True, padding='max_length', truncation=True, max_length=MAX_LENGTH)
  
  # since words might be split into subwords, labels need to be re-arranged
  # only the first subword has the label
  (...)
  tokens['labels'] = label_ids

  return tokens



tokens = []
for raw_text in data:
  clean_text, l = extract_labels(raw_text)
  t = tokenize_text(clean_text, l)
  tokens.append(t)


type(tokens[0])
# transformers.tokenization_utils_base.BatchEncoding
tokens[0]
# {'input_ids': [101, 69887, 10112, ..., 0, 0, 0], 'attention_mask': [1, 1, 1, ... 0, 0, 0], 'labels': [-100, 0, -100, ..., -100, -100, -100]}

Update, as asked, a basic example to reproduce:

from transformers import BertTokenizerFast
import tensorflow as tf
tokenizer = BertTokenizerFast.from_pretrained('bert-base-multilingual-uncased')
tokens = []
for text in ["Hello there", "Good morning"]:
  t = tokenizer(text.split(), is_split_into_words=True, padding='max_length', truncation=True, max_length=10)
  t['labels'] = list(map(lambda x: 1, t.word_ids())) # fake labels to simplify example
  tokens.append(t)

print(type(tokens[0])) # now tokens is a list of BatchEncodings
print(tokens)

If I directly tokenized the whole dataset I'd have a single BatchEnconding comprising everything, but I would not be able to handle the labels:

data = ["Hello there", "Good morning"]
tokens = tokenizer(data,  padding='max_length', truncation=True, max_length=10)
# now tokens is a batch encoding comprising all the dataset
print(type(tokens))
print(tokens)
# This way I can get a tf dataset like this:
tf.data.Dataset.from_tensor_slices(tokens)

Note that I need to first iterate the texts to get the labels and I need each text's word_ids() to rearrange the labels.

CodePudding user response：

You have a few options. You can use a defaultdict:

from collections import defaultdict
import tensorflow as tf

result = defaultdict(list)
for d in tokens:
    for k, v in d.items():
        result[k].append(v)

dataset = tf.data.Dataset.from_tensor_slices(dict(result))

Or you can use pandas as shown here:

import pandas as pd
import tensorflow as tf

dataset = tf.data.Dataset.from_tensor_slices(pd.DataFrame.from_dict(tokens).to_dict(orient="list"))

Or just create the correct structure while preprocessing your data:

from transformers import BertTokenizerFast
from collections import defaultdict
import tensorflow as tf

tokenizer = BertTokenizerFast.from_pretrained('bert-base-multilingual-uncased')
tokens = defaultdict(list)
for text in ["Hello there", "Good morning"]:
  t = tokenizer(text.split(), is_split_into_words=True, padding='max_length', truncation=True, max_length=10)
  tokens['input_ids'].append(t['input_ids'])
  tokens['token_type_ids'].append(t['token_type_ids'])
  tokens['attention_mask'].append(t['attention_mask'])
  t['labels'] = list(map(lambda x: 1, t.word_ids())) # fake labels to simplify example
  tokens['labels'].append(t['labels'])

dataset = tf.data.Dataset.from_tensor_slices(dict(tokens))
for x in dataset:
  print(x)

{'input_ids': <tf.Tensor: shape=(10,), dtype=int32, numpy=
array([  101, 29155, 10768,   102,     0,     0,     0,     0,     0,
           0], dtype=int32)>, 'token_type_ids': <tf.Tensor: shape=(10,), dtype=int32, numpy=array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(10,), dtype=int32, numpy=array([1, 1, 1, 1, 0, 0, 0, 0, 0, 0], dtype=int32)>, 'labels': <tf.Tensor: shape=(10,), dtype=int32, numpy=array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)>}
{'input_ids': <tf.Tensor: shape=(10,), dtype=int32, numpy=
array([  101, 12050, 17577,   102,     0,     0,     0,     0,     0,
           0], dtype=int32)>, 'token_type_ids': <tf.Tensor: shape=(10,), dtype=int32, numpy=array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(10,), dtype=int32, numpy=array([1, 1, 1, 1, 0, 0, 0, 0, 0, 0], dtype=int32)>, 'labels': <tf.Tensor: shape=(10,), dtype=int32, numpy=array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)>}