Interview-GPT Model Card Model Overview Model Name: Interview-GPT Author: Rahul Wale Version: 1.0 License: MIT

Description: Interview-GPT is a fine-tuned version of the FLAN-T5 model specifically designed to assist users in preparing for job interviews. The model is capable of generating contextually relevant responses based on user inputs, simulating a realistic interview environment. It aims to help candidates improve their interview skills by providing instant feedback and suggestions.

Intended Use This model is intended for:

Job seekers preparing for interviews across various industries. Career coaches and trainers seeking to provide interactive interview preparation sessions. Educational institutions offering career guidance services. Training Data The model was fine-tuned on the QuAC (Question Answering in Context) dataset, which consists of conversational data extracted from Wikipedia articles. This dataset provides a rich context for generating relevant interview questions and answers.

Model Architecture Interview-GPT is based on the FLAN-T5 architecture, which is a transformer-based model known for its flexibility in handling various NLP tasks. It leverages a sequence-to-sequence framework, allowing it to generate coherent responses to input queries.

Usage Instructions To use the Interview-GPT model, you can simply load it using the Hugging Face Transformers library:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

Load the model and tokenizer

tokenizer = AutoTokenizer.from_pretrained("Rahulwale12/Interview-gpt") model = AutoModelForSeq2SeqLM.from_pretrained("Rahulwale12/Interview-gpt")

Example usage

input_text = "Tell me about yourself." inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**inputs) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response)

Limitations While Interview-GPT is designed to assist with interview preparation, users should be aware of the following limitations:

The model may generate responses that are contextually accurate but not necessarily factually correct. The quality of the output may vary depending on the specificity and clarity of the input queries. The model is not a substitute for professional career counseling or guidance. Ethical Considerations Users should be mindful of the ethical implications of using AI-generated content, particularly in sensitive scenarios like job interviews. The model is intended to supplement human efforts and should not be solely relied upon for decision-making.

Acknowledgments We acknowledge the contributions of the Hugging Face community and the developers behind the FLAN-T5 architecture.