You can deploy this model and start earning money today!

adityabavadekar/
gpt2-medium-finetuned-qamodel

Name: gpt2-medium-finetuned-qamodel
Rating: 2.5 (9 reviews)

text-generation-inference

endpoints_compatible

Views

No views yet

Model Description

This conversational QA model is developed by Aditya Bavadekar. It is built upon the GPT-2 architecture, finetuned for the specific task of answer to the given questions.

Model Type: Conversational (Question to Answer)
Language(s) (NLP): English
Model Name: gpt2-medium-finetuned-qamodel
Fine-tuned from Model: gpt2

Model Sources

Repository: GitHub Repository
Colab Run: Google Colab Notebook

Bias, Risks, and Limitations

This model is currently in the testing phase and is likely to exhibit biases. Caution should be exercised when using the model's outputs for critical tasks.

Getting Started

You can quickly begin using the model by loading it or the associated pipeline using the transformers library:

python

from transformers import pipeline

generator = pipeline("text-generation", model="AdityaBavadekar/gpt2-medium-finetuned-qamodel")

Here are some recommended configurations that work well:

python

BEAM_SIZE = 5
TEMPERATURE = 2.0
MAX_LENGTH = 200
NOR_NGRAM = 2
PROMPT = """
Instruction: You are a respectful, friendly, helpful assistant.

Question : What is your name?
"""
generated_text = generator(
    PROMPT,
    max_length=MAX_LENGTH,
    temperature=TEMPERATURE,
    num_beams=BEAM_SIZE,
    no_repeat_ngram_size=NOR_NGRAM,
    early_stopping=True
)[0]["generated_text"]

print(generated_text)

Prompt Format

To interact with the model, use the following prompt format:

Instruction: [instruction_here]

Question : [question_here]

Training Details

Final QA Dataset Size: 57,283 Samples
GPUs: 1 (Tesla T4)
Learning Rate: 3e-4
Epochs: 3 (Training was halted prematurely due to time constraints)

Please do note that this model card provides an overview of the conversational QA model and guides on how to use it effectively. Keep in mind the model's limitations and potential biases while interpreting its outputs.

adityabavadekar/gpt2-medium-finetuned-qamodel