Summarization
Transformers
PyTorch
Safetensors
Russian
English
t5
text2text-generation
dialogue-summarization
Eval Results (legacy)
text-generation-inference
Instructions to use d0rj/rut5-base-summ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d0rj/rut5-base-summ 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="d0rj/rut5-base-summ")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("d0rj/rut5-base-summ") model = AutoModelForSeq2SeqLM.from_pretrained("d0rj/rut5-base-summ") - Inference
- Notebooks
- Google Colab
- Kaggle
Add evaluation results on the default config and test split of xsum
#3
by autoevaluator HF Staff - opened
Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋!
Your model has been evaluated on the default config and test split of the xsum dataset by @d0rj , 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.
d0rj changed pull request status to merged