---
tags:
- trl
- sft
- generated_from_trainer
- Text Generation
- llama
- t5
model-index:
- name: Prompt-Enhace-T5-base
  results: []
datasets:
- gokaygokay/prompt-enhancer-dataset
license: apache-2.0
language:
- en
base_model: google-t5/t5-base
library_name: transformers
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# omersaidd / Prompt-Enhace-T5-base 

This model was trained from scratch on an gokaygokay/prompt-enhancer-dataset dataset.

Bu modelin eğitiminde gokaygokay/prompt-enhancer-dataset veriseti kullanılmşıtır

## Model description

This model is trained with the google/t5-base and the database on prompt generation.

Bu model google/t5-base ile prompt üretimek üzerine veriseti ile eğitilmişitir

## Intended uses & limitations

More information needed

## Training and evaluation data

Kullandığımız verisetimiz gokaygokay/prompt-enhancer-dataset

Our dataset we use gokaygokay/prompt-enhancer-dataset

### Training hyperparameters

Eğitim sırasında aşağıdaki hiperparametreler kullanılmıştır:

The following hyperparameters were used during training:
- learning_rate: 3e-6
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Framework versions

- Transformers 4.43.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

## Test Model Code

```python
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)

enhancer = pipeline('text2text-generation',
                    model=model,
                    tokenizer=tokenizer,
                    repetition_penalty= 1.2,
                    device=device)

max_target_length = 256
prefix = "enhance prompt: "

short_prompt = "beautiful house with text 'hello'"
answer = enhancer(prefix + short_prompt, max_length=max_target_length)
final_answer = answer[0]['generated_text']
print(final_answer)
```