transformers onnx safetensors t5 text2text-generation prompt enhance flan summarization en zh base_model:google/flan-t5-small base_model:quantized:google/flan-t5-small license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model:
- google/flan-t5-small
language:
- en
- zh
library_name: transformers
license: apache-2.0
pipeline_tag: summarization
tags:
- prompt
- enhance
- flan
---
![xdfgzsxdfg.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/LUO0VbyTOGIp6pde17MJT.png)
# **t5-Flan-Prompt-Enhance**
T5-Flan-Prompt-Enhance is a fine-tuned model based on **Flan-T5-Small**, specifically designed to **enhance prompts, captions, and annotations**. This means the model has been further trained to improve the quality, clarity, and richness of textual inputs, making them more detailed and expressive.  

### Key Features:
1. **Prompt Expansion** – Takes short or vague prompts and enriches them with more context, depth, and specificity.  
2. **Caption Enhancement** – Improves captions by adding more descriptive details, making them more informative and engaging.  
3. **Annotation Refinement** – Enhances annotations by making them clearer, more structured, and contextually relevant.  

### Run with Transformers

```python
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

# Model checkpoint
model_checkpoint = "prithivMLmods/t5-Flan-Prompt-Enhance"

# Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

# Model
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)

enhancer = pipeline('text2text-generation',
                    model=model,
                    tokenizer=tokenizer,
                    repetition_penalty=1.2,
                    device=0 if device == "cuda" else -1)

max_target_length = 256
prefix = "enhance prompt: "

short_prompt = "three chimneys on the roof, green trees and shrubs in front of the house"
answer = enhancer(prefix + short_prompt, max_length=max_target_length)
final_answer = answer[0]['generated_text']
print(final_answer)
```

This fine-tuning process allows **T5-Flan-Prompt-Enhance** to generate **high-quality, well-structured, and contextually relevant outputs**, which can be particularly useful for tasks such as text generation, content creation, and AI-assisted writing.
