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README.md
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## t5-small-negative-prompt-generator
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This model [t5-small](https://huggingface.co/google-t5/t5-small) has been finetuned on a subset of the [AdamCodd/Civitai-8m-prompts](https://huggingface.co/datasets/AdamCodd/Civitai-8m-prompts) dataset (~800K prompts) focused on the top 10% prompts according to Civitai's positive engagement ("stats" field in the dataset).
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It achieves the following results on the evaluation set:
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* Loss: 0.1730
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## Usage
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The length of the negative prompt is adjustable with the `max_new_tokens` parameter. Keep in mind that you'll need to adjust the samplers slightly to avoid repetition and improve the quality of the output.
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```python
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from transformers import pipeline
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---
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## t5-small-negative-prompt-generator
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This model [t5-small](https://huggingface.co/google-t5/t5-small) has been finetuned on a subset of the [AdamCodd/Civitai-8m-prompts](https://huggingface.co/datasets/AdamCodd/Civitai-8m-prompts) dataset (~800K prompts) focused on the top 10% prompts according to Civitai's positive engagement ("stats" field in the dataset).
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It achieves the following results on the evaluation set:
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* Loss: 0.1730
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## Usage
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The length of the negative prompt is adjustable with the `max_new_tokens` parameter. Keep in mind that you'll need to adjust the samplers slightly to avoid repetition and improve the quality of the output. The dataset includes negative embeddings, so you'll find them in the output.
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```python
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from transformers import pipeline
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