|
|
--- |
|
|
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. |
|
|
|
