---
license: apache-2.0
datasets:
- Salesforce/wikitext
language:
- en
base_model:
- openai-community/gpt2
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
---

# **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

```py
pip install transformers
```

```py
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()
```

```py
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
```

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

# Print the generated text
print(generated_text)
```


---

### **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.

---

### **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* **Factual Inaccuracy**: May generate plausible-sounding but incorrect or outdated information.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.