mobenta/M_Prompter

Usage

from transformers import pipeline     

# load model and tokenizer from huggingface hub with pipeline
enhancer = pipeline("summarization", model="mobenta/M_Prompter", 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'])

Lamini-Prompt-Enchance-Long

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: 2.1624
• Rouge1: 20.2443
• Rouge2: 9.3642
• Rougel: 17.2484
• Rougelsum: 19.0703
• 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: 8
• 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
2.4435	1.0	2014	2.2723	20.0108	9.2736	17.0569	18.8171	19.0
2.341	2.0	4028	2.2120	20.4422	9.4473	17.4347	19.2234	19.0
2.2948	3.0	6042	2.1820	20.5645	9.5426	17.5419	19.3714	19.0
2.2598	4.0	8056	2.1668	20.2354	9.3639	17.2379	19.0625	19.0
2.2431	5.0	10070	2.1624	20.2443	9.3642	17.2484	19.0703	19.0

Framework versions

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