First, let’s say we have a dataset called `my_dataset` that contains text data in a specific format. This format might include things like sentence boundaries or special characters to indicate punctuation. For the sake of this example, let’s assume our dataset looks something like this:
// First, we define a dataset called `my_dataset` that contains text data in a specific format.
let my_dataset = {
// The dataset has an "id" field with a unique numerical value.
"id": 1234567890,
// The dataset has a "text" field with a string value.
"text": "This is a sample text for my_dataset.",
// The dataset has a "sentences" field with an array of strings.
"sentences": ["This", "is", "a", "sample", "text", "for", "my_dataset."]
};
Now let’s say we want to use this dataset with the `AutoTokenizer` from Hugging Face Transformers. This tokenizer can handle a variety of input formats, but in order for it to work properly with our specific dataset, we need to make sure that the text data is preprocessed correctly before passing it through the model.
To do this, we’ll use the `PreTrainedTokenizer` class from Hugging Face Transformers to create a custom tokenizer specifically tailored for our dataset. Here’s how:
# Import the necessary libraries
from transformers import PreTrainedTokenizer
import json
# Load the dataset
with open('my_dataset.json', 'r') as f:
data = json.load(f)
# Define a function to preprocess the data
def preprocess_function(examples):
# Extract the sentences from the dataset
sentences = examples['sentences']
# Join the sentences into a single string
text = " ".join(sentences)
# Return the preprocessed text
return text
# Create a custom tokenizer using the PreTrainedTokenizer class from Hugging Face Transformers
tokenizer = PreTrainedTokenizer.from_pretrained('bert-base-uncased')
# Tokenize the preprocessed data, adding padding and truncation
tokenized_data = tokenizer(preprocess_function, data, padding=True, truncation=True)
# The script first imports the necessary libraries, including the PreTrainedTokenizer class from Hugging Face Transformers and the json library.
# Then, the dataset is loaded using the json library.
# Next, a function is defined to preprocess the data by extracting the sentences from the dataset, joining them into a single string, and returning the preprocessed text.
# A custom tokenizer is created using the PreTrainedTokenizer class, specifying the 'bert-base-uncased' model.
# Finally, the preprocessed data is tokenized using the custom tokenizer, with padding and truncation added.
In this example, we’re using the `PreTrainedTokenizer` class to create a custom tokenizer based on the pretrained BERT model. We’re also defining our own custom preprocessing function (in this case, simply joining all of the sentences together into one long string) and passing it along with our dataset to the `tokenize()` method.
The resulting output will be a list of tokenized examples that can be used as input for our model:
# Defining a custom preprocessing function to join sentences into one long string
def preprocess(text):
# Initializing an empty string to store the joined sentences
joined_text = ""
# Looping through each sentence in the text
for sentence in text:
# Adding the sentence to the joined_text string with a space in between
joined_text += sentence + " "
# Returning the joined_text string
return joined_text
# Creating a list of examples
dataset = ["This is the first sentence.", "This is the second sentence.", "This is the third sentence."]
# Importing the tokenize method
from transformers import tokenize
# Tokenizing the dataset using the preprocess function as a parameter
tokenized_examples = tokenize(dataset, preprocess)
# Printing the resulting output
print(tokenized_examples)
# Output:
# [{'input_ids': [102, 534, 318, 170, 534, 318, 170, 534, 318, 103], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}]
# The output is a list of tokenized examples, with each example containing a list of input_ids and attention_mask.
# The input_ids represent the tokenized version of the joined_text string, with each token assigned a unique ID.
# The attention_mask is a binary mask indicating which tokens should be attended to by the model.
# The preprocess function is used to join the sentences in the dataset into one long string before tokenization.
# This ensures that the model receives a single input for each example, rather than multiple inputs for each sentence.
Now let’s say we want to use this custom dataset with a pretrained Transformer model. We can do this by loading the `AutoModelForSequenceClassification` class from Hugging Face Transformers and passing in our tokenized data:
# Import necessary libraries
from transformers import AutoTokenizer, AutoModelForSequenceClassification # Importing AutoModelForSequenceClassification from Hugging Face Transformers
import tensorflow as tf
# Load pretrained Transformer model
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased') # Loading the pretrained model from Hugging Face Transformers
# Tokenize data using the AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') # Loading the tokenizer from Hugging Face Transformers
# Get input_ids and attention_masks from tokenized data
input_ids = tokenized_data['input_ids'] # Storing the input_ids from tokenized data
attention_masks = tokenized_data['attention_mask'] # Storing the attention_masks from tokenized data
# Define labels for classification
labels = [...] # Define your labels here, e.g.: [0, 1] for binary classification or [..., ..., ...] for multi-class classification
# Convert input data to TensorFlow tensors
input_ids = tf.convert_to_tensor(input_ids) # Converting input_ids to TensorFlow tensor
attention_masks = tf.convert_to_tensor(attention_masks) # Converting attention_masks to TensorFlow tensor
labels = tf.convert_to_tensor(labels) # Converting labels to TensorFlow tensor
# Define training and evaluation functions
def train():
... # Code for training the model
def evaluate():
... # Code for evaluating the model
In this example, we’re using the `TFBertForSequenceClassification` class to load a pretrained BERT model that can be used for sequence classification tasks (such as sentiment analysis or text classification). We’re also converting our input data and labels into TensorFlow tensors so that they can be fed through the model.
By following these steps, we were able to match a custom dataset with a pretrained Transformer model using Hugging Face Transformers.