Summarization
Transformers
PyTorch
Core ML
ONNX
Safetensors
English
t5
text2text-generation
text-generation-inference
Instructions to use Falconsai/text_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Falconsai/text_summarization 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="Falconsai/text_summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Falconsai/text_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("Falconsai/text_summarization") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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license: apache-2.0
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# Model Card: Fine-Tuned T5 Small for Text Summarization
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References
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Hugging Face Model Hub
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T5 Paper
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Disclaimer: The model's performance may be influenced by the quality and representativeness of the data it was fine-tuned on. Users are encouraged to assess the model's suitability for their specific applications and datasets.
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license: apache-2.0
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language:
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- en
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pipeline_tag: summarization
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---
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# Model Card: Fine-Tuned T5 Small for Text Summarization
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References
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Hugging Face Model Hub
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T5 Paper
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Disclaimer: The model's performance may be influenced by the quality and representativeness of the data it was fine-tuned on. Users are encouraged to assess the model's suitability for their specific applications and datasets.
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