Gpt2-Wikitext-9180

Gpt2-Wikitext-9180

Gpt2-Wikitext-9180, fine-tuned from GPT-2, is a Transformer-based language model trained on a large English corpus (WikiText) using self-supervised learning. This means it was trained on raw, unlabeled text data, using an automated process to create inputs and labels by predicting the next word in a sentence. No manual annotation was involved, allowing the model to leverage a vast amount of publicly available data.

Demo Inference

pip install transformers

import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Loading pre-trained GPT-2 model and tokenizer
model_name = "prithivMLmods/Gpt2-Wikitext-9180"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

# Set the model to evaluation mode
model.eval()

def generate_text(prompt, max_length=100, temperature=0.8, top_k=50):
    input_ids = tokenizer.encode(prompt, return_tensors="pt")
    output = model.generate(
        input_ids,
        max_length=max_length,
        temperature=temperature,
        top_k=top_k,
        pad_token_id=tokenizer.eos_token_id,
        do_sample=True
    )
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    return generated_text

# Example prompt
prompt = "Once upon a time"
generated_text = generate_text(prompt, max_length=68)

# Print the generated text
print(generated_text)

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Intended Use Case

• Text Generation: Auto-completion, story generation, or dialogue simulation.
• Language Modeling: Understanding language structure and context for downstream NLP tasks.
• Educational and Research Use: Exploring fine-tuning techniques, language understanding, or benchmarking language models.
• Prototyping: Quick deployment of language-based features in applications and interfaces.

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Limitations

• Factual Inaccuracy: May generate plausible-sounding but incorrect or outdated information.
• Bias and Toxicity: Can reflect biases present in training data (e.g., stereotypes, offensive language).
• Context Length: Limited context window inherited from GPT-2 architecture.
• Not Real-Time Aware: Lacks access to current events or updates beyond its training data.
• Lack of Understanding: Generates text based on patterns, not genuine comprehension or reasoning.