Summarization
Transformers
Safetensors
Finnish
mbart
text2text-generation
mbart50
low-resource
research
rahvusarhiiv
Eval Results (legacy)
Instructions to use Rahvusarhiiv/fi_summariser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rahvusarhiiv/fi_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/fi_summariser")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Rahvusarhiiv/fi_summariser") model = AutoModelForSeq2SeqLM.from_pretrained("Rahvusarhiiv/fi_summariser", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "repo_id": "Rahvusarhiiv/fi_summariser", | |
| "repo_name": "fi_summariser", | |
| "language": "fi", | |
| "mbart_lang_code": "fi_FI", | |
| "base_model": "facebook/mbart-large-50-many-to-many-mmt", | |
| "local_model_dir": "outputs/dedicated_summarization_models/fi_mbart50_release_rebalanced_fi_archive_no_leak_v2/final_model", | |
| "training_metadata": "outputs/dedicated_summarization_models/fi_mbart50_release_rebalanced_fi_archive_no_leak_v2/training_metadata.json", | |
| "training_report": "outputs/summarization_training_data_release_rebalanced_fi_archive_no_leak_v1/split_report.md", | |
| "release_stance": "experimental; no SOTA claim", | |
| "eval_rows": [ | |
| { | |
| "label": "News-supported v2 automatic", | |
| "dataset": "news_supported_fi", | |
| "examples": 253, | |
| "rougeL_f": 0.22090510168567307, | |
| "rouge1_f": 0.24742374710032744, | |
| "rouge2_f": 0.14037243917776332, | |
| "reference_content_token_coverage": 0.25135526321861434, | |
| "prediction_source_content_token_precision": 0.8563214009522284, | |
| "prediction_words": 26.09090909090909, | |
| "repeated_4gram_rate": 0.0, | |
| "eu_legalese_template_flag": 0.0, | |
| "hit_max_new_tokens": 0.0, | |
| "kind": "automatic" | |
| }, | |
| { | |
| "label": "Wiki4 final automatic", | |
| "dataset": "wiki4_fi", | |
| "examples": 100, | |
| "rougeL_f": 0.36632625050723816, | |
| "rouge1_f": 0.4065308522110746, | |
| "rouge2_f": 0.2878370008587213, | |
| "reference_content_token_coverage": 0.39366923596637643, | |
| "prediction_source_content_token_precision": 0.8823272809018744, | |
| "prediction_words": 53.93, | |
| "repeated_4gram_rate": 0.0, | |
| "eu_legalese_template_flag": 0.0, | |
| "hit_max_new_tokens": 0.0, | |
| "kind": "automatic" | |
| }, | |
| { | |
| "label": "GDELT final automatic", | |
| "dataset": "gdelt_fi", | |
| "examples": 100, | |
| "rougeL_f": 0.11569887351597742, | |
| "rouge1_f": 0.1346809917233249, | |
| "rouge2_f": 0.044391688391582314, | |
| "reference_content_token_coverage": 0.12319378225476148, | |
| "prediction_source_content_token_precision": 0.8308603512930368, | |
| "prediction_words": 23.39, | |
| "repeated_4gram_rate": 0.0, | |
| "eu_legalese_template_flag": 0.0, | |
| "hit_max_new_tokens": 0.0, | |
| "kind": "automatic" | |
| } | |
| ] | |
| } | |