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--- |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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- Text Generation |
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- llama |
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- t5 |
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model-index: |
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- name: Prompt-Enhace-T5-base |
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results: [] |
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datasets: |
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- gokaygokay/prompt-enhancer-dataset |
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license: apache-2.0 |
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language: |
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- en |
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base_model: google-t5/t5-base |
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library_name: transformers |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# omersaidd / Prompt-Enhace-T5-base |
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This model was trained from scratch on an gokaygokay/prompt-enhancer-dataset dataset. |
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Bu modelin eğitiminde gokaygokay/prompt-enhancer-dataset veriseti kullanılmşıtır |
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## Model description |
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This model is trained with the google/t5-base and the database on prompt generation. |
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Bu model google/t5-base ile prompt üretimek üzerine veriseti ile eğitilmişitir |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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Kullandığımız verisetimiz gokaygokay/prompt-enhancer-dataset |
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Our dataset we use gokaygokay/prompt-enhancer-dataset |
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### Training hyperparameters |
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Eğitim sırasında aşağıdaki hiperparametreler kullanılmıştır: |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-6 |
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- train_batch_size: 256 |
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- eval_batch_size: 256 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 3 |
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### Framework versions |
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- Transformers 4.43.1 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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## Test Model Code |
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```python |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint) |
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enhancer = pipeline('text2text-generation', |
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model=model, |
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tokenizer=tokenizer, |
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repetition_penalty= 1.2, |
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device=device) |
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max_target_length = 256 |
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prefix = "enhance prompt: " |
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short_prompt = "beautiful house with text 'hello'" |
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answer = enhancer(prefix + short_prompt, max_length=max_target_length) |
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final_answer = answer[0]['generated_text'] |
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print(final_answer) |
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``` |
