Summarization
Transformers
PyTorch
Core ML
Safetensors
English
t5
text2text-generation
medical
text-generation-inference
Instructions to use Falconsai/medical_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Falconsai/medical_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/medical_summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Falconsai/medical_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("Falconsai/medical_summarization", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df23d804ee1306869c1c26086db737bfd070a1814bbe82d138f23480832bc42d
- Size of remote file:
- 242 MB
- SHA256:
- f1a6a370921cde2862afd981d62d6d6a06ffcf6739ca691d02d971e455915168
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