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---
license: apache-2.0
language:
- en
- zh
base_model:
- google/flan-t5-small
pipeline_tag: summarization
library_name: transformers
tags:
- prompt
- enhance
- flan
---
# **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.
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