---
library_name: transformers
tags: [text-generation, distilgpt2, fine-tuned, restaurant-reviews]
---

# Fine-Tuned DistilGPT2 on Restaurant Reviews

This is a fine-tuned version of [DistilGPT2](https://huggingface.co/distilgpt2) using a small dataset of restaurant reviews. The model is trained to generate human-like review completions given a text prompt.

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## Model Details

### Model Description

This model is a lightweight causal language model based on DistilGPT2, a distilled version of GPT2. It was fine-tuned on a small subset of restaurant reviews to help demonstrate how one can fine-tune and upload a model using Hugging Face and Google Colab with limited resources.

- **Developed by:** Sameer Jadaun (Fine-tuning)
- **Shared by:** [Sameer2407]
- **Model type:** Causal Language Model (Decoder-only transformer)
- **Language(s):** English
- **License:** Apache 2.0 (inherited from DistilGPT2)
- **Finetuned from model:** [distilgpt2](https://huggingface.co/distilgpt2)

---

## Uses

### Direct Use

You can use this model to generate restaurant reviews or autocomplete a review sentence given a starting prompt like:
> "The food was"

### Downstream Use

This model can be further fine-tuned on a larger corpus of restaurant, product, or service-related reviews to make it more robust and production-ready.

### Out-of-Scope Use

- Not suitable for factual QA tasks.
- Should not be used to generate harmful, toxic, or biased content.

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## Bias, Risks, and Limitations

This model was trained on a tiny dataset of restaurant reviews and may reflect language biases or poor generation quality due to undertraining. It's only for educational/demo purposes.

### Recommendations

- Avoid using in production.
- Fine-tune further with a more diverse and balanced dataset.

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## How to Get Started with the Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/fine-tuned-distilgpt2")
model = AutoModelForCausalLM.from_pretrained("your-username/fine-tuned-distilgpt2")

prompt = "The restaurant was"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50, do_sample=True)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))