Summarization
Transformers
Safetensors
Estonian
mbart
text2text-generation
mbart50
low-resource
research
rahvusarhiiv
Eval Results (legacy)
Instructions to use Rahvusarhiiv/et_summariser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rahvusarhiiv/et_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/et_summariser")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Rahvusarhiiv/et_summariser") model = AutoModelForSeq2SeqLM.from_pretrained("Rahvusarhiiv/et_summariser", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| coverage,dataset,eu_legalese_template_flag,examples,factuality,focus,hit_max_new_tokens,kind,label,language_score,overall,prediction_source_content_token_precision,prediction_words,reference_content_token_coverage,repeated_4gram_rate,rouge1_f,rouge2_f,rougeL_f | |
| 3.33,wiki4_et,,100,3.01,4.27,,ollama_judge,Wiki4 strict v2 LLM judge,4.3,2.73,,,,,,, | |
| 2.42,wiki4_et,,200,3.02,3.715,,ollama_judge,Wiki4 broad LLM judge,4.185,2.33,,,,,,, | |
| ,wiki4_et,0.0,100,,,0.0,automatic,Wiki4 final automatic,,,0.7509638190909874,62.39,0.38743965026667565,0.0,0.3513885249581917,0.20140574646547016,0.29355689324997836 | |
| ,gdelt_et,0.0,100,,,0.0,automatic,GDELT final automatic,,,0.8053276209384402,47.48,0.2569681719805297,0.0,0.21227974458081833,0.07460760477756037,0.1692342603250661 | |