Summarization
Transformers
PyTorch
English
t5
text2text-generation
medical
text-generation-inference
Instructions to use xtie/T5Score-PET with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xtie/T5Score-PET 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="xtie/T5Score-PET")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("xtie/T5Score-PET") model = AutoModelForSeq2SeqLM.from_pretrained("xtie/T5Score-PET", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5faefd7d84cabd81ce4981de2746e3e4abb8d404f7c9a3efb7111ab40382a14c
- Size of remote file:
- 3.13 GB
- SHA256:
- ac798c40f4978525e048aef081f42f08ebe7abca1858f0833c8747bfe4ce27e6
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