Summarization
Transformers
PyTorch
English
t5
text2text-generation
t5-large-summarization
pipeline:summarization
Eval Results (legacy)
text-generation-inference
Instructions to use sysresearch101/t5-large-finetuned-xsum-cnn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sysresearch101/t5-large-finetuned-xsum-cnn 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="sysresearch101/t5-large-finetuned-xsum-cnn")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sysresearch101/t5-large-finetuned-xsum-cnn") model = AutoModelForSeq2SeqLM.from_pretrained("sysresearch101/t5-large-finetuned-xsum-cnn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sysresearch101/t5-large-finetuned-xsum-cnn: direct link, hf CLI and curl.
- Browser
- Download file 2.95 GB
-
https://huggingface.co/sysresearch101/t5-large-finetuned-xsum-cnn/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sysresearch101/t5-large-finetuned-xsum-cnn/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sysresearch101/t5-large-finetuned-xsum-cnn/resolve/main/pytorch_model.bin
2.95 GB
- Xet hash:
- afd3bfced96bf357e1c7c91494c35c1d750344d6a966260a0cfb69483fc7ed2c
- Size of remote file:
- 2.95 GB
- SHA256:
- d9805407203ac26a45af46b35d0b0cdf292632052b3891cc90de39d3d59816c6
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