Summarization
Transformers
PyTorch
Enawené-Nawé
pegasus
text2text-generation
Trained with AutoTrain
Prompt Generation
prompt-generator
text generator
Instructions to use SamAct/PromptGeneration-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SamAct/PromptGeneration-base with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="SamAct/PromptGeneration-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("SamAct/PromptGeneration-base") model = AutoModelForSeq2SeqLM.from_pretrained("SamAct/PromptGeneration-base") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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tags:
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- autotrain
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- summarization
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language:
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- unk
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widget:
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emissions: 2.4412207269598545
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---
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# Model Trained Using AutoTrain
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- Problem type: Summarization
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tags:
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- autotrain
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- summarization
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- Prompt Generation
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language:
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- unk
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widget:
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emissions: 2.4412207269598545
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---
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## Usecase
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1. Prompt Generation.
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2. Title Generation.
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## Features
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Excellent accuracy for one line prompts. Prompts can be used for image generation, title or meta descriptions.
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# Model Trained Using AutoTrain
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- Problem type: Summarization
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