Instructions to use lennartschaeffer/TextSummarizerGenAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lennartschaeffer/TextSummarizerGenAI with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("lennartschaeffer/TextSummarizerGenAI") model = AutoModelForSeq2SeqLM.from_pretrained("lennartschaeffer/TextSummarizerGenAI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from lennartschaeffer/TextSummarizerGenAI: direct link, hf CLI and curl.
- Browser
- Download file 412 Bytes
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https://huggingface.co/lennartschaeffer/TextSummarizerGenAI/resolve/main/README.md
- Command line
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hf download hf://lennartschaeffer/TextSummarizerGenAI/README.md
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curl -L -o README.md https://huggingface.co/lennartschaeffer/TextSummarizerGenAI/resolve/main/README.md
412 Bytes
| library_name: transformers | |
| datasets: | |
| - EdinburghNLP/xsum | |
| base_model: | |
| - google/flan-t5-small | |
| # TextSummarizer AI | |
| <!-- Provide a quick summary of what the model is/does. --> | |
| Flan T5 small model fine tuned on BBC News articles for text summarization. | |
| - **Developed by:** Lennart Schaeffer | |
| - **Finetuned from model [optional]:** Flan T5 small | |
| ## Uses | |
| Summarizing and condensing long sequences of text. | |