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:
- ab1c6e5477c584c7fb7b5add851c332cfd1d10a199996df4797677ce2c5d6e6c
- Size of remote file:
- 1.52 GB
- SHA256:
- b954c0e5ee23f5803f5f84d57e69a408f1da7d50225ed24eadf598ce5a48a7ae
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.