Update README.md

README.md

@@ -1,8 +1,8 @@
 ---
 language:
-  - en
+- en
 bigbio_language:
-  - English
+- English
 license: cc0-1.0
 bigbio_license_shortname: CC0_1p0
 multilinguality: monolingual
@@ -11,7 +11,11 @@ homepage: https://github.com/Ineichen-Group/NeuroTrialNER/tree/main
 bigbio_pubmed: false
 bigbio_public: true
 bigbio_tasks:
-  - NAMED_ENTITY_RECOGNITION
+- NAMED_ENTITY_RECOGNITION
+task_categories:
+- token-classification
+tags:
+- medical
 ---
 
 
@@ -30,6 +34,14 @@ The corpus comprises 1093 clinical trial title and brief summaries from Clinical
 It has been annotated by two to three annotators for key trial characteristics, i.e., condition (e.g., Alzheimer's disease), 
 therapeutic intervention (e.g., aspirin), and control arms (e.g., placebo).
 
+## How to use
+
+```python
+from datasets import load_dataset
+data = load_dataset("bigbio/neurotrial_ner")
+```
+It includes three splits: `train` (787 examples), `validation` (153 examples), and `test` (153 examples). Each contains following fields: `nctid` (clinical trial identifier), `text` (original trial text), `tokens` (tokenized text), `token_bio_labels` (BIO-formatted token labels), and `entities` (annotated entity spans).
+
 ## Citation Information
 
 ```
@@ -55,4 +67,4 @@ therapeutic intervention (e.g., aspirin), and control arms (e.g., placebo).
     abstract = "Extracting and aggregating information from clinical trial registries could provide invaluable insights into the drug development landscape and advance the treatment of neurologic diseases. However, achieving this at scale is hampered by the volume of available data and the lack of an annotated corpus to assist in the development of automation tools. Thus, we introduce NeuroTrialNER, a new and fully open corpus for named entity recognition (NER). It comprises 1093 clinical trial summaries sourced from ClinicalTrials.gov, annotated for neurological diseases, therapeutic interventions, and control treatments. We describe our data collection process and the corpus in detail. We demonstrate its utility for NER using large language models and achieve a close-to-human performance. By bridging the gap in data resources, we hope to foster the development of text processing tools that help researchers navigate clinical trials data more easily.",
 }
 
-```
+```