Let’s say we have a dataset of news articles and questions related to those articles. We want to use DPRReader to find the article that best answers each question. Here’s how you might do it:
1. Preprocess your data by tokenizing the articles and questions, converting them into input_ids for DPRReader. 2. Load a pretrained model using `from_pretrained()` or initialize one with a config file (as shown in the original answer). 3. Feed each question through the model to get its representation as a pooler output. This is done by calling `forward(input_ids)`. 4. Find the article that has the closest representation to the question’s representation using some distance metric, such as cosine similarity or Euclidean distance. 5. Return the article and its corresponding index in your dataset. Here’s an example code snippet:
# Import necessary libraries
from transformers import DPRReader
import numpy as np
# Load pretrained model (or initialize with config file)
model = DPRReader.from_pretrained('dpr/bert-base') # Initialize model with pretrained weights
# Preprocess data into input_ids for the model
input_ids, attention_masks = prepare_data(questions, articles) # Call function to prepare data for model input
# Feed each question through the model to get its representation as a pooler output
outputs = []
for i in range(len(input_ids)): # Loop through each question
with torch.no_grad(): # Disable gradient calculation for faster inference
outputs.append(model(input_ids[i], attention_masks[i])['pooler_output']) # Get pooler output for each question and append to outputs list
# Find the article that has the closest representation to each question's representation using cosine similarity
similarities = []
for i in range(len(questions)): # Loop through each question
for j in range(len(articles)): # Loop through each article
if i != j: # Avoid comparing a question with itself (which would always have a score of 1)
sim = np.dot(outputs[i], outputs[j]) / (np.linalg.norm(outputs[i]) * np.linalg.norm(outputs[j])) # Calculate cosine similarity between question and article representations
similarities.append((sim, j)) # Append similarity score and article index to similarities list
# Sort the articles by their similarity scores to each question and return the top result
sorted_results = sorted(similarities, key=lambda x: x[0], reverse=True)[:1] # Sort similarities list in descending order and limit to only one result per question (for simplicity)
for i in range(len(questions)): # Loop through each question
print("Question:", questions[i]) # Print question
print("Article:", articles[sorted_results[i][1]]) # Print article with highest similarity score to question
In this example, we first load a pretrained DPRReader model using `from_pretrained()`. We then prepare our data by tokenizing the articles and questions into input_ids for the model. Next, we feed each question through the model to get its representation as a pooler output. Then, we find the article that has the closest representation to each question’s representation using cosine similarity. Finally, we print out the top result (i.e., the article with the highest similarity score) for each question.
If not, you can use a preprocessing function to convert text into input_ids using `from_pretrained()` or initialize one with a config file (as shown in the original answer).