Project 3: Sentiment Analysis API with Fine-Tuned Transformer
Step 2: Load and Preprocess the Dataset
Use the Hugging Face datasets library to load and preprocess the IMDb dataset. This library provides a simple interface to access and manipulate datasets, making it easy to work with large-scale data collections. The preprocessing step involves loading the raw text data, converting it into a format suitable for machine learning, and preparing it for model training.
The library handles common preprocessing tasks like tokenization (breaking text into smaller units), padding (making sequences uniform length), and truncation (limiting sequence length) automatically. This standardization ensures the data is in the correct format for the transformer model to process efficiently.
from datasets import load_dataset
from transformers import AutoTokenizer
# Load IMDb dataset
dataset = load_dataset("imdb")
# Initialize tokenizer
model_name = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Tokenize the dataset
def tokenize_function(examples):
return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=256)
tokenized_datasets = dataset.map(tokenize_function, batched=True)Let's break down this code:
1. Library Imports
- The code imports two essential libraries: 'datasets' for loading the IMDb dataset and 'AutoTokenizer' from transformers for text preprocessing
2. Dataset Loading
- The code loads the IMDb dataset using the Hugging Face datasets library, which contains 50,000 movie reviews split between positive and negative sentiments
3. Tokenizer Initialization
- It initializes a DistilBERT tokenizer, which is a lighter version of BERT, using the "distilbert-base-uncased" model
4. Tokenization Function
- The code defines a tokenize_function that processes the text data with these specifications:
- Truncation: Cuts off text that's too long
- Padding: Makes all sequences the same length (256 tokens)
- Max length: Sets the maximum sequence length to 256 tokens
5. Dataset Processing
- Finally, it applies the tokenization function to the entire dataset using the map function with batched processing enabled
This preprocessing step is crucial as it standardizes the text data into a format that the transformer model can process efficiently
Step 2: Load and Preprocess the Dataset
Use the Hugging Face datasets library to load and preprocess the IMDb dataset. This library provides a simple interface to access and manipulate datasets, making it easy to work with large-scale data collections. The preprocessing step involves loading the raw text data, converting it into a format suitable for machine learning, and preparing it for model training.
The library handles common preprocessing tasks like tokenization (breaking text into smaller units), padding (making sequences uniform length), and truncation (limiting sequence length) automatically. This standardization ensures the data is in the correct format for the transformer model to process efficiently.
from datasets import load_dataset
from transformers import AutoTokenizer
# Load IMDb dataset
dataset = load_dataset("imdb")
# Initialize tokenizer
model_name = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Tokenize the dataset
def tokenize_function(examples):
return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=256)
tokenized_datasets = dataset.map(tokenize_function, batched=True)Let's break down this code:
1. Library Imports
- The code imports two essential libraries: 'datasets' for loading the IMDb dataset and 'AutoTokenizer' from transformers for text preprocessing
2. Dataset Loading
- The code loads the IMDb dataset using the Hugging Face datasets library, which contains 50,000 movie reviews split between positive and negative sentiments
3. Tokenizer Initialization
- It initializes a DistilBERT tokenizer, which is a lighter version of BERT, using the "distilbert-base-uncased" model
4. Tokenization Function
- The code defines a tokenize_function that processes the text data with these specifications:
- Truncation: Cuts off text that's too long
- Padding: Makes all sequences the same length (256 tokens)
- Max length: Sets the maximum sequence length to 256 tokens
5. Dataset Processing
- Finally, it applies the tokenization function to the entire dataset using the map function with batched processing enabled
This preprocessing step is crucial as it standardizes the text data into a format that the transformer model can process efficiently
Step 2: Load and Preprocess the Dataset
Use the Hugging Face datasets library to load and preprocess the IMDb dataset. This library provides a simple interface to access and manipulate datasets, making it easy to work with large-scale data collections. The preprocessing step involves loading the raw text data, converting it into a format suitable for machine learning, and preparing it for model training.
The library handles common preprocessing tasks like tokenization (breaking text into smaller units), padding (making sequences uniform length), and truncation (limiting sequence length) automatically. This standardization ensures the data is in the correct format for the transformer model to process efficiently.
from datasets import load_dataset
from transformers import AutoTokenizer
# Load IMDb dataset
dataset = load_dataset("imdb")
# Initialize tokenizer
model_name = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Tokenize the dataset
def tokenize_function(examples):
return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=256)
tokenized_datasets = dataset.map(tokenize_function, batched=True)Let's break down this code:
1. Library Imports
- The code imports two essential libraries: 'datasets' for loading the IMDb dataset and 'AutoTokenizer' from transformers for text preprocessing
2. Dataset Loading
- The code loads the IMDb dataset using the Hugging Face datasets library, which contains 50,000 movie reviews split between positive and negative sentiments
3. Tokenizer Initialization
- It initializes a DistilBERT tokenizer, which is a lighter version of BERT, using the "distilbert-base-uncased" model
4. Tokenization Function
- The code defines a tokenize_function that processes the text data with these specifications:
- Truncation: Cuts off text that's too long
- Padding: Makes all sequences the same length (256 tokens)
- Max length: Sets the maximum sequence length to 256 tokens
5. Dataset Processing
- Finally, it applies the tokenization function to the entire dataset using the map function with batched processing enabled
This preprocessing step is crucial as it standardizes the text data into a format that the transformer model can process efficiently
Step 2: Load and Preprocess the Dataset
Use the Hugging Face datasets library to load and preprocess the IMDb dataset. This library provides a simple interface to access and manipulate datasets, making it easy to work with large-scale data collections. The preprocessing step involves loading the raw text data, converting it into a format suitable for machine learning, and preparing it for model training.
The library handles common preprocessing tasks like tokenization (breaking text into smaller units), padding (making sequences uniform length), and truncation (limiting sequence length) automatically. This standardization ensures the data is in the correct format for the transformer model to process efficiently.
from datasets import load_dataset
from transformers import AutoTokenizer
# Load IMDb dataset
dataset = load_dataset("imdb")
# Initialize tokenizer
model_name = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Tokenize the dataset
def tokenize_function(examples):
return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=256)
tokenized_datasets = dataset.map(tokenize_function, batched=True)Let's break down this code:
1. Library Imports
- The code imports two essential libraries: 'datasets' for loading the IMDb dataset and 'AutoTokenizer' from transformers for text preprocessing
2. Dataset Loading
- The code loads the IMDb dataset using the Hugging Face datasets library, which contains 50,000 movie reviews split between positive and negative sentiments
3. Tokenizer Initialization
- It initializes a DistilBERT tokenizer, which is a lighter version of BERT, using the "distilbert-base-uncased" model
4. Tokenization Function
- The code defines a tokenize_function that processes the text data with these specifications:
- Truncation: Cuts off text that's too long
- Padding: Makes all sequences the same length (256 tokens)
- Max length: Sets the maximum sequence length to 256 tokens
5. Dataset Processing
- Finally, it applies the tokenization function to the entire dataset using the map function with batched processing enabled
This preprocessing step is crucial as it standardizes the text data into a format that the transformer model can process efficiently