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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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- es |
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license: mit |
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model-index: |
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- name: es_BreastCancerNER |
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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: 0.9245630175 |
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- name: NER Recall |
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type: recall |
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value: 0.9396914446 |
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- name: NER F Score |
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type: f_score |
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value: 0.9320658474 |
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--- |
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Breast Cancer Diagnosis NER model |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `es_BreastCancerNER` | |
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| **Version** | `0.0.0` | |
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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** | n/a | |
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| **License** | `mit` | |
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| **Author** | [Álvaro García Barragán](https://huggingface.co/Alvaro8gb/es_BreastCancerNER) | |
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### Label Scheme |
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<details> |
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<summary>View label scheme (21 labels for 1 components)</summary> |
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| Component | Labels | |
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| --- | --- | |
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| **`ner`** | `CANCER_CONCEPT`, `CANCER_EXP`, `CANCER_GRADE`, `CANCER_INTRTYPE`, `CANCER_LOC`, `CANCER_MET`, `CANCER_REC`, `CANCER_STAGE`, `CANCER_SUBTYPE`, `CANCER_TYPE`, `DATE`, `IMPLICIT_DATE`, `MOLEC_MARKER`, `SURGERY`, `TNM`, `TRAT`, `TRAT_DRUG`, `TRAT_FREQ`, `TRAT_INTERVAL`, `TRAT_QUANTITY`, `TRAT_SHEMA` | |
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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` | 93.21 | |
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| `ENTS_P` | 92.46 | |
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| `ENTS_R` | 93.97 | |
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| `TRANSFORMER_LOSS` | 45014.63 | |
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| `NER_LOSS` | 1216054.67 | |
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## Citation |
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If you use our work in your research, please cite it as follows: |
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```bibtex |
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@INPROCEEDINGS{garcia-barraganCBMS2023, |
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author={García-Barragán, Alvaro and Solarte-Pabón, Oswaldo and Nedostup, Georgiy and Provencio, Mariano and Menasalvas, Ernestina and Robles, Victor}, |
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booktitle={2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)}, |
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title={Structuring Breast Cancer Spanish Electronic Health Records Using Deep Learning}, |
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year={2023}, |
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pages={404-409}, |
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keywords={Natural Language Processing (NLP), Information extraction, Deep Learning, Breast cancer.}, |
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doi={10.1109/CBMS58004.2023.00252} |
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} |
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``` |
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