Chapter 9: Implementing Transformer Models with Popular Libraries
9.3 Text Classification with Hugging Face’s Transformers Library
Text classification is a crucial task in natural language processing (NLP) applications. It is concerned with identifying the appropriate category of a text document or a segment of it, which could be in the form of a web page, news article, review, tweet, or any other textual data.
The task of text classification could be a binary classification where the text is classified into one of two categories such as positive or negative, spam or not spam. Additionally, it could also be a multiclass classification where the text is classified into one of multiple categories such as news, sports, politics, and so on.
Moreover, in some cases, text classification could also be a multilabel classification where the text is classified into more than one category. This task of text classification plays a pivotal role in many NLP applications, such as sentiment analysis, spam detection, topic classification, and so on.
Example:
Let's walk through an example of a binary classification problem: sentiment analysis. We will use the BERT model for this task:
from transformers import BertTokenizer, BertForSequenceClassification
from torch.nn import Softmax
import torch
# Initialize the tokenizer and model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
# The sentence to be classified
sentence = 'I love learning about Hugging Face Transformers.'
# Tokenize the sentence and convert to PyTorch tensors
inputs = tokenizer(sentence, return_tensors='pt')
# Forward pass through the model
outputs = model(**inputs)
# Apply softmax to the output logits to get probabilities
probabilities = Softmax(dim=1)(outputs.logits)
# Show probabilities
print(probabilities)In this example, we use the BERT model for sequence classification (BertForSequenceClassification), which is a normal BERT model with an added single linear layer on top for classification. We set num_labels to 2, corresponding to 'positive' or 'negative'. After running the model on our inputs, we apply softmax to the output logits to get probabilities for each class.
Please note, you need a labeled dataset to fine-tune the BERT model on the specific classification task. This example assumes that we are using a pre-trained and fine-tuned BERT model.
Now, we have seen how to use the Transformers library for a simple text classification task. This is just the tip of the iceberg. The Transformers library provides interfaces to many other models and tasks. In the next sections, we'll delve into how you can use the library for tasks such as named entity recognition and question answering.
9.3 Text Classification with Hugging Face’s Transformers Library
Text classification is a crucial task in natural language processing (NLP) applications. It is concerned with identifying the appropriate category of a text document or a segment of it, which could be in the form of a web page, news article, review, tweet, or any other textual data.
The task of text classification could be a binary classification where the text is classified into one of two categories such as positive or negative, spam or not spam. Additionally, it could also be a multiclass classification where the text is classified into one of multiple categories such as news, sports, politics, and so on.
Moreover, in some cases, text classification could also be a multilabel classification where the text is classified into more than one category. This task of text classification plays a pivotal role in many NLP applications, such as sentiment analysis, spam detection, topic classification, and so on.
Example:
Let's walk through an example of a binary classification problem: sentiment analysis. We will use the BERT model for this task:
from transformers import BertTokenizer, BertForSequenceClassification
from torch.nn import Softmax
import torch
# Initialize the tokenizer and model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
# The sentence to be classified
sentence = 'I love learning about Hugging Face Transformers.'
# Tokenize the sentence and convert to PyTorch tensors
inputs = tokenizer(sentence, return_tensors='pt')
# Forward pass through the model
outputs = model(**inputs)
# Apply softmax to the output logits to get probabilities
probabilities = Softmax(dim=1)(outputs.logits)
# Show probabilities
print(probabilities)In this example, we use the BERT model for sequence classification (BertForSequenceClassification), which is a normal BERT model with an added single linear layer on top for classification. We set num_labels to 2, corresponding to 'positive' or 'negative'. After running the model on our inputs, we apply softmax to the output logits to get probabilities for each class.
Please note, you need a labeled dataset to fine-tune the BERT model on the specific classification task. This example assumes that we are using a pre-trained and fine-tuned BERT model.
Now, we have seen how to use the Transformers library for a simple text classification task. This is just the tip of the iceberg. The Transformers library provides interfaces to many other models and tasks. In the next sections, we'll delve into how you can use the library for tasks such as named entity recognition and question answering.
9.3 Text Classification with Hugging Face’s Transformers Library
Text classification is a crucial task in natural language processing (NLP) applications. It is concerned with identifying the appropriate category of a text document or a segment of it, which could be in the form of a web page, news article, review, tweet, or any other textual data.
The task of text classification could be a binary classification where the text is classified into one of two categories such as positive or negative, spam or not spam. Additionally, it could also be a multiclass classification where the text is classified into one of multiple categories such as news, sports, politics, and so on.
Moreover, in some cases, text classification could also be a multilabel classification where the text is classified into more than one category. This task of text classification plays a pivotal role in many NLP applications, such as sentiment analysis, spam detection, topic classification, and so on.
Example:
Let's walk through an example of a binary classification problem: sentiment analysis. We will use the BERT model for this task:
from transformers import BertTokenizer, BertForSequenceClassification
from torch.nn import Softmax
import torch
# Initialize the tokenizer and model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
# The sentence to be classified
sentence = 'I love learning about Hugging Face Transformers.'
# Tokenize the sentence and convert to PyTorch tensors
inputs = tokenizer(sentence, return_tensors='pt')
# Forward pass through the model
outputs = model(**inputs)
# Apply softmax to the output logits to get probabilities
probabilities = Softmax(dim=1)(outputs.logits)
# Show probabilities
print(probabilities)In this example, we use the BERT model for sequence classification (BertForSequenceClassification), which is a normal BERT model with an added single linear layer on top for classification. We set num_labels to 2, corresponding to 'positive' or 'negative'. After running the model on our inputs, we apply softmax to the output logits to get probabilities for each class.
Please note, you need a labeled dataset to fine-tune the BERT model on the specific classification task. This example assumes that we are using a pre-trained and fine-tuned BERT model.
Now, we have seen how to use the Transformers library for a simple text classification task. This is just the tip of the iceberg. The Transformers library provides interfaces to many other models and tasks. In the next sections, we'll delve into how you can use the library for tasks such as named entity recognition and question answering.
9.3 Text Classification with Hugging Face’s Transformers Library
Text classification is a crucial task in natural language processing (NLP) applications. It is concerned with identifying the appropriate category of a text document or a segment of it, which could be in the form of a web page, news article, review, tweet, or any other textual data.
The task of text classification could be a binary classification where the text is classified into one of two categories such as positive or negative, spam or not spam. Additionally, it could also be a multiclass classification where the text is classified into one of multiple categories such as news, sports, politics, and so on.
Moreover, in some cases, text classification could also be a multilabel classification where the text is classified into more than one category. This task of text classification plays a pivotal role in many NLP applications, such as sentiment analysis, spam detection, topic classification, and so on.
Example:
Let's walk through an example of a binary classification problem: sentiment analysis. We will use the BERT model for this task:
from transformers import BertTokenizer, BertForSequenceClassification
from torch.nn import Softmax
import torch
# Initialize the tokenizer and model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
# The sentence to be classified
sentence = 'I love learning about Hugging Face Transformers.'
# Tokenize the sentence and convert to PyTorch tensors
inputs = tokenizer(sentence, return_tensors='pt')
# Forward pass through the model
outputs = model(**inputs)
# Apply softmax to the output logits to get probabilities
probabilities = Softmax(dim=1)(outputs.logits)
# Show probabilities
print(probabilities)In this example, we use the BERT model for sequence classification (BertForSequenceClassification), which is a normal BERT model with an added single linear layer on top for classification. We set num_labels to 2, corresponding to 'positive' or 'negative'. After running the model on our inputs, we apply softmax to the output logits to get probabilities for each class.
Please note, you need a labeled dataset to fine-tune the BERT model on the specific classification task. This example assumes that we are using a pre-trained and fine-tuned BERT model.
Now, we have seen how to use the Transformers library for a simple text classification task. This is just the tip of the iceberg. The Transformers library provides interfaces to many other models and tasks. In the next sections, we'll delve into how you can use the library for tasks such as named entity recognition and question answering.