| | --- |
| | tags: |
| | - spacy |
| | - token-classification |
| | language: |
| | - da |
| | license: apache-2.0 |
| | model-index: |
| | - name: da_dacy_large_DANSK_ner |
| | results: |
| | - task: |
| | name: NER |
| | type: token-classification |
| | metrics: |
| | - name: NER Precision |
| | type: precision |
| | value: 0.8100263852 |
| | - name: NER Recall |
| | type: recall |
| | value: 0.8203072812 |
| | - name: NER F Score |
| | type: f_score |
| | value: 0.8151344175 |
| | --- |
| | |
| | <a href="https://github.com/centre-for-humanities-computing/Dacy"><img src="https://centre-for-humanities-computing.github.io/DaCy/_static/icon.png" width="175" height="175" align="right" /></a> |
| |
|
| | # DaCy_large_DANSK_ner |
| | |
| | DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analyzing Danish pipelines. |
| | At the time of publishing this model, also included in DaCy encorporates the only models for fine-grained NER using DANSK dataset - a dataset containing 18 annotation types in the same format as Ontonotes. |
| | Moreover, DaCy's largest pipeline has achieved State-of-the-Art performance on Named entity recognition, part-of-speech tagging and dependency parsing for Danish on the DaNE dataset. |
| | Check out the [DaCy repository](https://github.com/centre-for-humanities-computing/DaCy) for material on how to use DaCy and reproduce the results. |
| | DaCy also contains guides on usage of the package as well as behavioural test for biases and robustness of Danish NLP pipelines. |
| | |
| | |
| | | Feature | Description | |
| | | --- | --- | |
| | | **Name** | `da_dacy_large_DANSK_ner` | |
| | | **Version** | `0.1.0` | |
| | | **spaCy** | `>=3.5.0,<3.6.0` | |
| | | **Default Pipeline** | `transformer`, `ner` | |
| | | **Components** | `transformer`, `ner` | |
| | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | |
| | | **Sources** | DANSK - Danish Annotations for NLP Specific TasKs<br />KennethEnevoldsen/dfm-bert-large-v1-2048bsz-1Msteps (Kenneth Enevoldsen) | |
| | | **License** | `apache-2.0` | |
| | | **Author** | [Centre for Humanities Computing Aarhus](https://chcaa.io/#/) | |
| | |
| | ### Label Scheme |
| | |
| | <details> |
| | |
| | <summary>View label scheme (18 labels for 1 components)</summary> |
| | |
| | | Component | Labels | |
| | | --- | --- | |
| | | **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FACILITY`, `GPE`, `LANGUAGE`, `LAW`, `LOCATION`, `MONEY`, `NORP`, `ORDINAL`, `ORGANIZATION`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK OF ART` | |
| | |
| | </details> |
| | |
| | ### Accuracy |
| | |
| | | Type | Score | |
| | | --- | --- | |
| | | `ENTS_F` | 81.51 | |
| | | `ENTS_P` | 81.00 | |
| | | `ENTS_R` | 82.03 | |
| | | `TRANSFORMER_LOSS` | 63375.61 | |
| | | `NER_LOSS` | 158164.20 | |
| |
|
| |
|
| | ### Performance tables |
| |
|
| | The table below shows the F1, recall and precision of the three DaCy fine-grained models. |
| | | Score | DaCy large | DaCy medium | DaCy small | |
| | |:---:|:---:|:---:|:---:| |
| | | F1 | 0.823 | 0.806 | 0.776 | |
| | | Recall | 0.834 | 0.818 | 0.77 | |
| | | Precision | 0.813 | 0.794 | 0.781 | |
| |
|
| | The table below shows the F1 of the three DaCy fine-grained models within each named entity type. |
| | | Named-entity type | DaCy large | DaCy medium | DaCy small | |
| | |:---:|:---:|:---:|:---:| |
| | | CARDINAL | 0.874 | 0.781 | 0.887 | |
| | | DATE | 0.846 | 0.859 | 0.867 | |
| | | EVENT | 0.611 | 0.571 | 0.4 | |
| | | FACILITY | 0.545 | 0.533 | 0.468 | |
| | | GPE | 0.893 | 0.838 | 0.794 | |
| | | LANGUAGE | 0.902 | 0.486 | 0.194 | |
| | | LAW | 0.686 | 0.625 | 0.606 | |
| | | LOCATION | 0.633 | 0.737 | 0.581 | |
| | | MONEY | 0.993 | 1 | 0.947 | |
| | | NORP | 0.78 | 0.887 | 0.785 | |
| | | ORDINAL | 0.696 | 0.7 | 0.727 | |
| | | ORGANIZATION | 0.863 | 0.851 | 0.781 | |
| | | PERCENT | 0.923 | 0.96 | 0.96 | |
| | | PERSON | 0.871 | 0.872 | 0.833 | |
| | | PRODUCT | 0.671 | 0.635 | 0.526 | |
| | | QUANTITY | 0.386 | 0.654 | 0.708 | |
| | | TIME | 0.643 | 0.571 | 0.71 | |
| | | WORK OF ART | 0.494 | 0.639 | 0.488 | |
| |
|
| | The table below shows the F1 of the three DaCy fine-grained models within each domain of texts in DANSK. |
| | | Domain | DaCy large | DaCy medium | DaCy small | |
| | |:---:|:---:|:---:|:---:| |
| | | All domains combined | 0.823 | 0.806 | 0.776 | |
| | | Conversation | 0.796 | 0.718 | 0.82 | |
| | | Dannet | 0.75 | 0.667 | 1 | |
| | | Legal | 0.852 | 0.854 | 0.866 | |
| | | News | 0.841 | 0.759 | 0.86 | |
| | | Social Media | 0.793 | 0.847 | 0.8 | |
| | | Web | 0.826 | 0.802 | 0.756 | |
| | | Wiki and Books | 0.778 | 0.838 | 0.709 | |
| |
|
