Summarization
Transformers
PyTorch
Safetensors
Russian
English
mbart
text2text-generation
dialogue-summarization
mbart-50
Instructions to use d0rj/ru-mbart-large-summ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d0rj/ru-mbart-large-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/ru-mbart-large-summ")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("d0rj/ru-mbart-large-summ") model = AutoModelForSeq2SeqLM.from_pretrained("d0rj/ru-mbart-large-summ") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4cd8944728b518fa1e44aa6b5b24fad145985cf6cc48a8dcf51d255409ca3b3
- Size of remote file:
- 4.02 kB
- SHA256:
- 84219ca0e24d2f1f360c4b0f7fc37fcd7d4d64751c5030f63d1ab8f88ed4e5eb
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