How to use DPRReader for text classification in Python

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Here’s how you use it: first, install the simpletransformers library by running this command in your terminal: `pip install simpletransformers`. Then, download a dataset of text data (like movie reviews or product descriptions) and store it as a CSV file somewhere on your computer. Next, load that data into a pandas DataFrame using Python’s built-in `read_csv()` function.
Once you have your data loaded up in the DataFrame, you can use DPRReader to classify each text sample based on whether it’s positive or negative. Here’s an example code snippet that shows how this works:

# Import necessary libraries
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import pandas as pd
import torch

# Define the model name and load the pre-trained model
model_name = "distilbert-base-uncased-finetuned-sst-2-english"
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Load the tokenizer for the model
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load the movie dataset into a pandas DataFrame and view its columns using the `head()` function:
movie_data = pd.read_csv('/path/to/your/dataset')
movie_data.head()

# Use the model to classify each text sample in the dataset as positive or negative:
for index, row in movie_data.iterrows():
    # Get the text data for this particular review (stored as a string):
    text = row['review']
    
    # Preprocess the text by converting it into a list of tokens using the tokenizer function:
    encoded_text = tokenizer.encode_plus(text, return_tensors='pt')
    
    # Pass the preprocessed text through the model to get its predictions:
    output = model(**encoded_text)
    
    # Get the predicted probability for each class (positive or negative):
    logits = output.logits
    probabilities = torch.softmax(logits, dim=1).detach().numpy()
    
    # Print out the text and its predicted probability:
    print("Review:", text)
    print("Probability of being positive:", round(probabilities[0][1], 2))

# Explanation:
# The first line imports the necessary libraries for the script to run.
# The next two lines define the model name and load the pre-trained model using the AutoModelForSequenceClassification function.
# The following line loads the tokenizer for the model using the AutoTokenizer function.
# The next line loads the movie dataset into a pandas DataFrame and uses the head() function to view its columns.
# The for loop iterates through each row in the dataset and performs the following steps:
# - Gets the text data for the review.
# - Preprocesses the text by converting it into a list of tokens using the tokenizer function.
# - Passes the preprocessed text through the model to get its predictions.
# - Gets the predicted probability for each class (positive or negative).
# - Prints out the text and its predicted probability.
# The encode_plus function in the preprocessing step is used instead of the tokenizer function to properly tokenize the text and add special tokens for the model to understand.
# The output.logits in the prediction step is used to get the raw output from the model, which is then converted to probabilities using the softmax function.
# The probabilities are then rounded to two decimal places for better readability.
# The script uses the torch library to perform these operations on the pre-trained model.

And that’s it! With DPRReader, you can easily classify your text data based on whether it’s positive or negative. It uses some fancy algorithms to do this, but all you need to know is how to install the library and run a few lines of code.

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