Summarization
Transformers
Safetensors
Russian
t5
text2text-generation
fred-t5
russian
research
rahvusarhiiv
Eval Results (legacy)
text-generation-inference
Instructions to use Rahvusarhiiv/ru_summariser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rahvusarhiiv/ru_summariser 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="Rahvusarhiiv/ru_summariser")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Rahvusarhiiv/ru_summariser") model = AutoModelForSeq2SeqLM.from_pretrained("Rahvusarhiiv/ru_summariser", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 583 Bytes
ce7edae | 1 2 3 4 5 6 | benchmark,automatic_n,judge_n,rouge1_f,rouge2_f,rougeL_f,reference_content_token_coverage,prediction_source_content_token_precision,prediction_words,judge_factuality,judge_coverage,judge_focus,judge_language,judge_overall
wiki4_ru,30,10,0.452985,0.393261,0.430705,0.408086,0.969713,50.2,4.5,3.5,4.9,5.0,4.0
gdelt_ru,30,10,0.243889,0.117618,0.202185,0.414623,0.955032,56.0,5.0,4.3,4.7,5.0,4.5
news_supported_ru,30,10,0.318805,0.143452,0.227585,0.321147,0.812335,48.6,4.3,4.3,4.8,4.9,4.2
macro,90,30,0.338560,0.218110,0.286825,0.381285,0.912360,51.6,4.6,4.033333,4.8,4.966667,4.233333
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