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--- |
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tags: |
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- spacy |
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- token-classification |
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language: |
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- da |
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license: apache-2.0 |
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model-index: |
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- name: da_dacy_large_ner_fine_grained |
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results: |
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- task: |
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name: NER |
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type: token-classification |
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metrics: |
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- name: NER Precision |
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type: precision |
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value: 0.813029316 |
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- name: NER Recall |
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type: recall |
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value: 0.8336673347 |
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- name: NER F Score |
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type: f_score |
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value: 0.8232189974 |
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datasets: |
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- chcaa/DANSK |
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--- |
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<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> |
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# DaCy_large_ner_fine_grained |
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DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analyzing Danish pipelines. |
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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. |
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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. |
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Check out the [DaCy repository](https://github.com/centre-for-humanities-computing/DaCy) for material on how to use DaCy and reproduce the results. |
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DaCy also contains guides on usage of the package as well as behavioural test for biases and robustness of Danish NLP pipelines. |
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For information about the use of this model as well as guides to its use, please refer to [DaCys documentation](https://centre-for-humanities-computing.github.io/DaCy/using_dacy.html). |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `da_dacy_large_ner_fine_grained` | |
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| **Version** | `0.1.0` | |
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| **spaCy** | `>=3.5.0,<3.6.0` | |
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| **Default Pipeline** | `transformer`, `ner` | |
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| **Components** | `transformer`, `ner` | |
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| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | |
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| **Sources** | [DANSK - Danish Annotations for NLP Specific TasKs](https://huggingface.co/datasets/chcaa/DANSK) (chcaa)<br />[chcaa/dfm-encoder-large-v1](https://huggingface.co/chcaa/dfm-encoder-large-v1) (CHCAA) | |
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| **License** | `apache-2.0` | |
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| **Author** | [Centre for Humanities Computing Aarhus](https://chcaa.io/#/) | |
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### Label Scheme |
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<details> |
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<summary>View label scheme (18 labels for 1 components)</summary> |
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| Component | Labels | |
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| --- | --- | |
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| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FACILITY`, `GPE`, `LANGUAGE`, `LAW`, `LOCATION`, `MONEY`, `NORP`, `ORDINAL`, `ORGANIZATION`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK OF ART` | |
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</details> |
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### Accuracy |
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| Type | Score | |
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| --- | --- | |
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| `ENTS_F` | 82.32 | |
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| `ENTS_P` | 81.30 | |
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| `ENTS_R` | 83.37 | |
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| `TRANSFORMER_LOSS` | 41138.73 | |
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| `NER_LOSS` | 103772.53 | |
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### Training |
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For progression in loss and performance on the dev set during training, please refer to the Weights and Biases run, [HERE](https://wandb.ai/emil-tj/dacy-an-efficient-pipeline-for-danish/runs/b2wv5ah9?workspace=user-emil-tj) |
