gokaygokay/Lamini-Prompt-Enchance

Usage

from transformers import pipeline     

# load model and tokenizer from huggingface hub with pipeline
enhancer = pipeline("summarization", model="gokaygokay/Lamini-Prompt-Enchance", device=0)

prompt = "A blue-tinted bedroom scene, surreal and serene, with a mysterious reflected interior."
prefix = "Enhance the description: "
# enhance prompt
res = enhancer(prefix + prompt)

print(res[0]['summary_text'])

# A surreal and serene bedroom scene with a mysterious mirrored interior, 
# awash in blue and green hues.
# The room is adorned with intricate patterns and a mirrored wall, 
# creating a sense of mystery and tranquility.

Lamini-Prompt-Enchance

This model is a fine-tuned version of MBZUAI/LaMini-Flan-T5-248M on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.0195
• Rouge1: 31.5042
• Rouge2: 13.2633
• Rougel: 26.4176
• Rougelsum: 28.4846
• Gen Len: 19.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 24
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	115	2.1369	31.6298	13.2671	26.4264	28.5472	19.0
No log	2.0	230	2.0733	31.4969	13.2677	26.5009	28.4785	19.0
No log	3.0	345	2.0405	31.4735	13.01	26.1931	28.3299	19.0
No log	4.0	460	2.0250	31.4761	13.2096	26.3479	28.3059	19.0
2.2448	5.0	575	2.0195	31.5042	13.2633	26.4176	28.4846	19.0

Framework versions

• Transformers 4.42.3
• Pytorch 2.3.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1

Citation and attribution

This model release is maintained by Gökay Aydoğan. If you reference this repository in academic work, please cite it as follows and also cite the upstream models, datasets, or projects it builds upon.

@software{aydogan2024lamini_prompt_enchance,
  author = {Aydoğan, Gökay},
  title = {{Lamini-Prompt-Enchance}},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance},
  note = {Model repository; cite the base model and upstream datasets as required.}
}