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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
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