model_data_json/Alvenir_bert-punct-restoration-da.json

{
    "model_id": "Alvenir/bert-punct-restoration-da",
    "downloads": 11269,
    "tags": [
        "transformers",
        "pytorch",
        "bert",
        "token-classification",
        "punctuation restoration",
        "da",
        "dataset:custom",
        "license:apache-2.0",
        "autotrain_compatible",
        "endpoints_compatible",
        "region:us"
    ],
    "description": "--- language: da tags: - bert - punctuation restoration license: apache-2.0 datasets: - custom --- # Bert Punctuation Restoration Danish This model performs the punctuation restoration task in Danish. The method used is sequence classification similar to how NER models are trained. ## Model description TODO ### How to use The model requires some additional inference code, hence we created an awesome little pip package for inference. The inference code is based on the pipeline from huggingface. First, install the little package by running Then restoration is as simple as the following snippet: ## Training data To Do ## Training procedure To Do ### Preprocessing TODO ## Evaluation results TODO",
    "model_explanation_gemini": "Restores punctuation in Danish text using BERT-based sequence classification.\n\nFeatures:  \n- Language: Danish (da)  \n- Task: Punctuation restoration  \n- Architecture: BERT  \n- Training method: Sequence classification (NER-like approach)  \n- License: Apache-2.0  \n- Custom dataset  \n\n(No comparative analysis provided in the original description)",
    "release_year": null,
    "parameter_count": null,
    "is_fine_tuned": false,
    "category": "Named Entity Recognition",
    "model_family": "BERT",
    "api_enhanced": true
}