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:
- 3c3275a12756c5ae76d05d237a3bc15d206909024e42af054cfa784c9997f2c3
- Size of remote file:
- 242 MB
- SHA256:
- 54169aa8f9153f4c640acf50dacfd01effea19d7af7763534bd9ec99cee00d0e
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