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
Add evaluation results on the 3.0.0 config of cnn_dailymail
#2
by autoevaluator HF Staff - opened
Beep boop, I am a bot from Hugging Face's automatic model evaluator π!
Your model has been evaluated on the 3.0.0 config of the cnn_dailymail dataset by @sysresearch101 , using the predictions stored here.
Accept this pull request to see the results displayed on the Hub leaderboard.
Evaluate your model on more datasets here.