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 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sysresearch101/t5-large-finetuned-xsum 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")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sysresearch101/t5-large-finetuned-xsum") model = AutoModelForSeq2SeqLM.from_pretrained("sysresearch101/t5-large-finetuned-xsum") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#8 opened 9 months ago
by
SFconvertbot
Adding `safetensors` variant of this model
#7 opened over 1 year ago
by
SFconvertbot
Add evaluation results on the default config and test split of xsum
#6 opened over 2 years ago
by
autoevaluator
Adding `safetensors` variant of this model
#5 opened about 3 years ago
by
SFconvertbot
Add evaluation results on the 3.0.0 config of cnn_dailymail
#3 opened almost 4 years ago
by
autoevaluator
Add evaluation results on the 3.0.0 config of cnn_dailymail
#2 opened almost 4 years ago
by
autoevaluator