Summarization
Transformers
Safetensors
Latvian
mbart
text2text-generation
mbart50
low-resource
research
rahvusarhiiv
Eval Results (legacy)
Instructions to use Rahvusarhiiv/lv_summariser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rahvusarhiiv/lv_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/lv_summariser")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Rahvusarhiiv/lv_summariser") model = AutoModelForSeq2SeqLM.from_pretrained("Rahvusarhiiv/lv_summariser", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "repo_id": "Rahvusarhiiv/lv_summariser", | |
| "repo_name": "lv_summariser", | |
| "release": "revision2_hf_curated_v1", | |
| "published": "2026-07", | |
| "previous_release_tag": "v1", | |
| "language": "lv", | |
| "mbart_lang_code": "lv_LV", | |
| "base_model": "facebook/mbart-large-50-many-to-many-mmt", | |
| "local_model_dir": "outputs/dedicated_summarization_models/lv_mbart50_revision2_hf_curated_v1/final_model", | |
| "training_metadata": "outputs/dedicated_summarization_models/lv_mbart50_revision2_hf_curated_v1/training_metadata.json", | |
| "training_report": "outputs/summarization_training_data_revision2_hf_curated/split_report.md", | |
| "comparison_report": "outputs/summarization_revision2_vs_hf_comparison/comparison_report.md", | |
| "release_stance": "experimental; no SOTA claim", | |
| "train_rows": 30000, | |
| "validation_rows": 708, | |
| "seed": 20260713, | |
| "eval_results_file": "eval_results.json" | |
| } | |