metadata
license: cc-by-nc-4.0
base_model: MBZUAI/LaMini-Flan-T5-248M
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: Lamini-Prompt-Enchance
results: []
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