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Unleashing Passive-Aggressive AI: Detecting and Rewriting Email Tones with BERT, GPT-2, and LoRA Have you ever received an email that just rubbed you the wrong way? Maybe it was a little too passive-aggressive for your liking. Well, fear not, because with the power of NLP models like BERT and GPT-2, we can now automatically detect the tone of emails and rewrite them in a more passive-aggressive manner. In this GitHub repository, you'll find an NLP pipeline that does just that. The project overview includes two main tasks: tone detection using a fine-tuned bert-base-uncased model and email rewriting using a fine-tuned gpt2 model with LoRA. Let's dive into the details and see how it all works. Code Snippets: Here's a snippet of the code for tone detection using BERT: # Import necessary libraries import pandas as pd from transformers import BertTokenizer, BertForSequenceClassification # Load fine-tuned BERT model model = BertForSequenceClassification.from_pretrained('bert-base-uncased') tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') # Load email dataset df = pd.read_csv('tone_dataset.csv') # Tokenize and classify email tones # (Code for tokenization and classification goes here) And here's a snippet of the code for email rewriting using GPT-2 with LoRA: # Import necessary libraries from transformers import GPT2LMHeadModel, GPT2Tokenizer # Load fine-tuned GPT-2 model with LoRA model = GPT2LMHeadModel.from_pretrained('gpt2') tokenizer = GPT2Tokenizer.from_pretrained('gpt2') # Load email dataset df = pd.read_csv('tone_dataset.csv') # Generate passive-aggressive email responses # (Code for response generation goes here) Examples: Here are a couple of examples of emails before and after being rewritten in a passive-aggressive manner: Original Email: Subject: Meeting Cancelled Hi team, Just wanted to let you know that the meeting scheduled for tomorrow has been cancelled. Apologies for any inconvenience this may cause. Thanks, [Sender] Rewritten Email: Subject: Meeting Cancelled (AGAIN) Hi team, Just breaking the news that the meeting scheduled for tomorrow has been cancelled. I hope you all have a backup plan, unlike some people who rely solely on meetings for productivity. Thanks, [Sender] Conclusion: With the power of advanced NLP models like BERT and GPT-2, we can now automatically detect the tone of emails and rewrite them in a more passive-aggressive manner. This GitHub repository provides a comprehensive pipeline for achieving this, including fine-tuning models and generating rewritten emails. So the next time you receive an email that's a little too polite for your taste, just run it through our pipeline and watch the passive-aggressive magic happen. Happy emailing!
Unleashing Passive-Aggressive AI: Detecting and Rewriting Email Tones with BERT, GPT-2, and LoRA
Have you ever received an email that just rubbed you the wrong way? Maybe it was a little too passive-aggressive for your liking. Well, fear not, because with the power of NLP models like BERT and GPT-2, we can now automatically detect the tone of emails and rewrite them in a more passive-aggressive manner.
In this GitHub repository, you'll find an NLP pipeline that does just that. The project overview includes two main tasks: tone detection using a fine-tuned bert-base-uncased model and email rewriting using a fine-tuned gpt2 model with LoRA. Let's dive into the details and see how it all works.
Code Snippets:
Here's a snippet of the code for tone detection using BERT:
# Import necessary libraries
import pandas as pd
from transformers import BertTokenizer, BertForSequenceClassification
# Load fine-tuned BERT model
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# Load email dataset
df = pd.read_csv('tone_dataset.csv')
# Tokenize and classify email tones
# (Code for tokenization and classification goes here)
And here's a snippet of the code for email rewriting using GPT-2 with LoRA:
# Import necessary libraries
from transformers import GPT2LMHeadModel, GPT2Tokenizer
# Load fine-tuned GPT-2 model with LoRA
model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# Load email dataset
df = pd.read_csv('tone_dataset.csv')
# Generate passive-aggressive email responses
# (Code for response generation goes here)
Examples:
Here are a couple of examples of emails before and after being rewritten in a passive-aggressive manner:
Original Email:
Subject: Meeting Cancelled
Hi team,
Just wanted to let you know that the meeting scheduled for tomorrow has been cancelled. Apologies for any inconvenience this may cause.
Thanks,
[Sender]
Rewritten Email:
Subject: Meeting Cancelled (AGAIN)
Hi team,
Just breaking the news that the meeting scheduled for tomorrow has been cancelled. I hope you all have a backup plan, unlike some people who rely solely on meetings for productivity.
Thanks,
[Sender]
Conclusion:
With the power of advanced NLP models like BERT and GPT-2, we can now automatically detect the tone of emails and rewrite them in a more passive-aggressive manner. This GitHub repository provides a comprehensive pipeline for achieving this, including fine-tuning models and generating rewritten emails. So the next time you receive an email that's a little too polite for your taste, just run it through our pipeline and watch the passive-aggressive magic happen. Happy emailing!
