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Car-ing is sharing, an auto dealership company for car sales and rental, is taking their services to the next level thanks to Large Language Models (LLMs).
As their newly recruited AI and NLP developer, you've been asked to prototype a chatbot app with multiple functionalities that not only assist customers but also provide support to human agents in the company.
The solution should receive textual prompts and use a variety of pre-trained Hugging Face LLMs to respond to a series of tasks, e.g. classifying the sentiment in a car’s text review, answering a customer question, summarizing or translating text, etc.
Before you start
In order to complete the project you may wish to install some Hugging Face libraries such as transformers and evaluate.
!pip install transformers
!pip install evaluate
from transformers import logging
logging.set_verbosity(logging.WARNING)Hidden output
# Start your code here!
import pandas as pd
import torch
file_path = "data/car_reviews.csv"
df = pd.read_csv(file_path, delimiter=";")
reviews = df['Review'].tolist()
real_labels = df['Class'].tolist()
# Load a sentiment analysis LLM into a pipeline
from transformers import pipeline
classifier = pipeline('sentiment-analysis', model='distilbert-base-uncased-finetuned-sst-2-english')
# Perform inference on the car reviews and display prediction results
predicted_labels = classifier(reviews)
for review, prediction, label in zip(reviews, predicted_labels, real_labels):
print(f"Review: {review}\nActual Sentiment: {label}\nPredicted Sentiment: {prediction['label']} (Confidence: {prediction['score']:.4f})\n")
# Load accuracy and F1 score metrics
import evaluate
accuracy = evaluate.load("accuracy")
f1 = evaluate.load("f1")
# Map categorical sentiment labels into integer labels
references = [1 if label == "POSITIVE" else 0 for label in real_labels]
predictions = [1 if label['label'] == "POSITIVE" else 0 for label in predicted_labels]
# Calculate accuracy and F1 score
accuracy_result_dict = accuracy.compute(references=references, predictions=predictions)
accuracy_result = accuracy_result_dict['accuracy']
f1_result_dict = f1.compute(references=references, predictions=predictions)
f1_result = f1_result_dict['f1']
print(f"Accuracy: {accuracy_result}")
print(f"F1 result: {f1_result}")
# Load translation LLM into a pipeline and translate car review
first_review = reviews[0]
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")
translated_review = translator(first_review, max_length=27)[0]['translation_text']
print(f"Model translation:\n{translated_review}")
# Load reference translations from file
with open("data/reference_translations.txt", 'r') as file:
lines = file.readlines()
references = [line.strip() for line in lines]
print(f"Spanish translation references:\n{references}")
# Load and calculate BLEU score metric
bleu = evaluate.load("bleu")
bleu_score = bleu.compute(predictions=[translated_review], references=[references])
print(bleu_score['bleu'])
from transformers import AutoTokenizer
from transformers import AutoModelForQuestionAnswering
# Instantiate model and tokenizer
model_ckp = "deepset/minilm-uncased-squad2"
tokenizer = AutoTokenizer.from_pretrained(model_ckp)
model = AutoModelForQuestionAnswering.from_pretrained(model_ckp)
# Define context and question, and tokenize them
context = reviews[1]
print(f"Context:\n{context}")
question = "What did he like about the brand?"
inputs = tokenizer(question, context, return_tensors="pt")
# Perform inference and extract answer from raw outputs
with torch.no_grad():
outputs = model(**inputs)
start_idx = torch.argmax(outputs.start_logits)
end_idx = torch.argmax(outputs.end_logits) + 1
answer_span = inputs["input_ids"][0][start_idx:end_idx]
# Decode and show answer
answer = tokenizer.decode(answer_span)
print("Answer: ", answer)
# Get original text to summarize upon car review
text_to_summarize = reviews[-1]
print(f"Original text:\n{text_to_summarize}")
# Load summarization pipeline and perform inference
model_name = "cnicu/t5-small-booksum"
summarizer = pipeline("summarization", model=model_name)
outputs = summarizer(text_to_summarize, max_length=53)
summarized_text = outputs[0]['summary_text']
print(f"Summarized text:\n{summarized_text}")