Create README.md

README.md

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+---
+language:
+- en
+- fr
+- gal
+- it
+- pt
+- ro
+multilinguality:
+- multilingual
+license: cc-by-4.0
+task_categories:
+- token-classification
+pretty_name: LivingNER
+config_names:
+- en
+- fr
+- gal
+- it
+- pt
+- ro
+- combined
+
+dataset_info:
+- config_name: en
+  splits:
+  - name: train
+    num_bytes: 14699476
+    num_examples: 1000
+  - name: validation
+    num_bytes: 6764942
+    num_examples: 500
+- config_name: fr
+  splits:
+  - name: train
+    num_bytes: 14699476
+    num_examples: 1000
+  - name: validation
+    num_bytes: 6764942
+    num_examples: 500
+- config_name: gal
+  splits:
+  - name: train
+    num_bytes: 14699476
+    num_examples: 1000
+  - name: validation
+    num_bytes: 6764942
+    num_examples: 500
+- config_name: it
+  splits:
+  - name: train
+    num_bytes: 14699476
+    num_examples: 1000
+  - name: validation
+    num_bytes: 6764942
+    num_examples: 500
+- config_name: pt
+  splits:
+  - name: train
+    num_bytes: 14699476
+    num_examples: 1000
+  - name: validation
+    num_bytes: 6764942
+    num_examples: 500
+- config_name: ro
+  splits:
+  - name: train
+    num_bytes: 14699476
+    num_examples: 1000
+  - name: validation
+    num_bytes: 6764942
+    num_examples: 500
+- config_name: combined
+  splits:
+  - name: train
+    num_bytes: 108745150,
+    num_examples: 7000
+  - name: validation
+    num_bytes: 50100231
+    num_examples: 3500
+    
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: en/train/data-*
+  - split: validation
+    path: en/validation/data-*
+- config_name: en
+  data_files:
+  - split: train
+    path: en/train/data-*
+  - split: validation
+    path: en/validation/data-*
+- config_name: fr
+  data_files:
+  - split: train
+    path: fr/train/data-*
+  - split: validation
+    path: fr/validation/data-*
+- config_name: gal
+  data_files:
+  - split: train
+    path: gal/train/data-*
+  - split: validation
+    path: gal/validation/data-*
+- config_name: it
+  data_files:
+  - split: train
+    path: it/train/data-*
+  - split: validation
+    path: it/validation/data-*
+- config_name: pt
+  data_files:
+  - split: train
+    path: pt/train/data-*
+  - split: validation
+    path: pt/validation/data-*
+- config_name: ro
+  data_files:
+  - split: train
+    path: ro/train/data-*
+  - split: validation
+    path: ro/validation/data-*
+- config_name: combined
+  data_files:
+  - split: train
+    path: combined/train/data-*
+  - split: validation
+    path: combined/validation/data-*
+---
+
+
+# LivingNER: Named entity recognition, normalization & classification of species, pathogens and food
+
+
+### Dataset Summary
+
+The LivingNER Gold Standard corpus is a collection of 2000 clinical case reports covering a broad range of medical specialities, i.e. infectious diseases (including Covid-19 cases), cardiology, neurology, oncology, dentistry, pediatrics, endocrinology, primary care, allergology, radiology, psychiatry, ophthalmology, urology, internal medicine, emergency and intensive care medicine, tropical medicine, and dermatology annotated with species [SPECIES] (including living organisms and microorganisms) and infectious diseases [ENFERMEDAD] mentions. Species mentions include many pathogens and infectious agents, but also food, allergens, pets or other species, taxonomic groups and organisms of clinical relevance. 
+
+The  LivingNER corpus has also annotations of mentions of humans (tag HUMAN), including the patients itself, family members, healhcare professionals or other persons mentioned in the case reports. Thus it can be useful to extract family history information of patients or information about the social and healthcare personal environment and interactions.
+
+All mentions have been exhaustively manually mapped by experts to their corresponding (NCBI Taxonomy)[https://www.ncbi.nlm.nih.gov/taxonomy] identifiers. 
+
+It was used for the (LivingNER)[https://temu.bsc.es/livingner/] Shared Task on pathogens and living beings detection and normalization in Spanish medical documents, which was celebrated as part of IberLEF 2022.
+
+
+## Dataset Description
+
+- **Languages:**
+  - en
+  - fr
+  - gal
+  - it
+  - pt
+  - ro
+  - combined
+- **Training Set Size:** {1000} examples
+- **Test Set Size:** {500} examples
+- **Features:**
+  - text: Original text
+  - language: Language identifier
+  - tokens: Tokenized text
+  - ner_tags: Named entity tags in BIO format
+  - entity_mentions: Detailed entity information
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset('path/to/dataset', '{lang}')
+
+# Access splits
+train_data = dataset['train']
+test_data = dataset['test']
+```
+
+## Labels
+
+The following entity types are annotated in this dataset:
+['O', 'B-HUMAN', 'I-HUMAN', 'B-SPECIES', 'I-SPECIES']
+
+### Citation Information
+```json
+@article{amiranda2022nlp,
+  title={Mention detection, normalization \& classification of species, pathogens, humans and food in clinical documents: Overview of LivingNER shared task and resources},
+  author={Miranda-Escalada, Antonio and Farr{\'e}-Maduell, Eul{`a}lia and Lima-L{\'o}pez, Salvador and Estrada, Darryl and Gasc{\'o}, Luis and Krallinger, Martin},
+  journal = {Procesamiento del Lenguaje Natural}, year={2022}
+}
+
+@dataset{miranda_escalada_2022_7684093,
+  author       = {Miranda-Escalada, Antonio and
+                  Farré-Maduell, Eulàlia and
+                  Lima-López, Salvador and
+                  González Gacio, Gloria and
+                  Krallinger, Martin},
+  title        = {LivingNER corpus: Named entity recognition,
+                   normalization \& classification of species,
+                   pathogens and food
+                  },
+  month        = jun,
+  year         = 2022,
+  publisher    = {Zenodo},
+  version      = {6.3.1},
+  doi          = {10.5281/zenodo.7684093},
+  url          = {https://doi.org/10.5281/zenodo.7684093},
+}
+```