Update README.md

README.md

@@ -1,3 +1,32 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- bigbio/neurotrial_ner
+base_model:
+- michiyasunaga/BioLinkBERT-base
+pipeline_tag: token-classification
+---
+
+## Intended uses & limitations
+
+#### How to use
+
+You can use this model with Transformers *pipeline* for NER.
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("simonada/NeuroTrialNER_BioLinkBERT")
+model = AutoModelForTokenClassification.from_pretrained("simonada/NeuroTrialNER_BioLinkBERT")
+
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "This trial examines atypical antipsychotic aripiprazole as an augmenting agent to antidepressant therapy in treatment-resistant depressed patients."
+
+ner_results = nlp(example)
+print(ner_results)
+```
+
+#### Limitations and bias
+
+This model is limited by its training dataset of entity-annotated clinical trial registry records from a specific span of time and focused on the field of neuroscience. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases.