create model

README.md

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+---
+tags:
+- flair
+- entity-mention-linker
+---
+
+## biobert-ncbi-disease
+
+Biomedical Entity Mention Linking for diseases
+
+### Demo: How to use in Flair
+
+Requires:
+- **[Flair](https://github.com/flairNLP/flair/)>=0.14.0** (`pip install flair` or `pip install git+https://github.com/flairNLP/flair.git`)
+
+
+```python
+from flair.data import Sentence
+from flair.models import Classifier, EntityMentionLinker
+
+sentence = Sentence("Behavioral abnormalities in the Fmr1 KO2 Mouse Model of Fragile X Syndrome")
+
+# load hunflair to detect the entity mentions we want to link.
+tagger = Classifier.load("hunflair")
+tagger.predict(sentence)
+
+# load the linker and dictionary
+linker = EntityMentionLinker.load("helpmefindaname/flair-eml-biobert-ncbi-disease")
+dictionary = linker.dictionary
+
+# find then candidates for the mentions
+linker.predict(sentence)
+
+# print the results for each entity mention:
+for span in sentence.get_spans(linker.entity_label_type):
+    print(f"Span: {span.text}")
+    for candidate_label in span.get_labels(linker.label_type):
+        candidate = dictionary[candidate_label.value]
+        print(f"Candidate: {candidate.concept_name}")
+```
+
+
+As an alternative to downloading the already precomputed model (much storage). You can also build the model
+and compute the embeddings for the dataset using:
+```python
+linker = EntityMentionLinker.build("dmis-lab/biosyn-biobert-ncbi-disease", "diseases", dictionary_name_or_path="ctd-diseases", hybrid_search=False, entity_type="diseases-eml")
+```
+This will reduce the download requirements, at the cost of computation.
+
+This EntityMentionLinker uses [https://huggingface.co/dmis-lab/biosyn-biobert-ncbi-disease](dmis-lab/biosyn-biobert-ncbi-disease) as embeddings for linking mentions to candidates.
+

pytorch_model.bin

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