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Masih Moafi
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email generator.ipynb
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134
email generator.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "609b9e9d-7f2b-49c3-b08f-97e6e5259242",
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"metadata": {},
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"outputs": [
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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"Enter the professor's name: Ali Asadpour\n",
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"Enter your research topic: AI in architecture\n",
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"Enter your name: Masih Moafi\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\Josep\\anaconda3\\envs\\myenv\\lib\\site-packages\\transformers\\generation\\configuration_utils.py:492: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.7` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
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" warnings.warn(\n",
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"C:\\Users\\Josep\\anaconda3\\envs\\myenv\\lib\\site-packages\\transformers\\generation\\configuration_utils.py:497: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
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" warnings.warn(\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Generated Email:\n",
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"\n",
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"Dear Professor Ali Asadpour,\n",
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"\n",
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" I am writing to express my interest in pursuing research under your guidance. My research topic revolves around AI in architecture.\n",
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"\n",
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" I believe that your work in this area is groundbreaking, and I am eager to contribute to your ongoing projects.\n",
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"\n",
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" Best regards,\n",
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" Masih Moafi\n",
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" _________________________________________\n"
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]
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}
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],
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"source": [
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"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
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"\n",
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"# Load the tokenizer and model for GPT-J\n",
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"tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/gpt-j-6B\")\n",
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"model = AutoModelForCausalLM.from_pretrained(\"EleutherAI/gpt-j-6B\")\n",
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"\n",
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"def generate_email(professor_name, research_topic, user_name):\n",
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" \"\"\"\n",
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" Generate a professional and customizable email using GPT-J.\n",
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" Args:\n",
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" professor_name (str): The professor's name.\n",
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" research_topic (str): The user's research topic.\n",
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" user_name (str): The user's name.\n",
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" Returns:\n",
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" str: The generated email text.\n",
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" \"\"\"\n",
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" # Email template\n",
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" prompt = f\"\"\"\n",
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" Dear Professor {professor_name},\n",
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"\n",
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" I am writing to express my interest in pursuing research under your guidance. My research topic revolves around {research_topic}.\n",
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"\n",
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" I believe that your work in this area is groundbreaking, and I am eager to contribute to your ongoing projects.\n",
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"\n",
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" Best regards,\n",
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" {user_name}\n",
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" \"\"\"\n",
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"\n",
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" # Encode input\n",
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" input_ids = tokenizer.encode(prompt, return_tensors=\"pt\")\n",
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"\n",
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" # Generate email with controlled randomness\n",
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" output = model.generate(\n",
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" input_ids,\n",
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" max_length=len(input_ids[0]) + 100, # Slightly extend length to avoid truncation\n",
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" do_sample=False, # Disable sampling for deterministic output\n",
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" temperature=0.7, # Control output randomness\n",
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" top_p=0.9, # Use nucleus sampling for coherent generation\n",
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" pad_token_id=tokenizer.eos_token_id # Prevent padding issues\n",
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" )\n",
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"\n",
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" # Decode and return the text\n",
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" generated_email = tokenizer.decode(output[0], skip_special_tokens=True)\n",
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" return generated_email.strip()\n",
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"\n",
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"# Input data\n",
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"professor_name = input(\"Enter the professor's name: \")\n",
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"research_topic = input(\"Enter your research topic: \")\n",
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"user_name = input(\"Enter your name: \")\n",
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"\n",
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"# Generate and print the email\n",
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"email = generate_email(professor_name, research_topic, user_name)\n",
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"print(\"\\nGenerated Email:\\n\")\n",
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"print(email)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f575bf56-5146-49df-803a-f1ceecdbc963",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.19"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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