Create README.md

README.md

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+---
+tags:
+  - flair
+  - entity-mention-linker
+---
+
+## sapbert-ncbi-taxonomy-no-ab3p
+
+Biomedical Entity Mention Linking for UMLS.
+We use this model for species since NCBI Taxonomy is contained in UMLS:
+
+- Model:  [cambridgeltl/SapBERT-from-PubMedBERT-fulltext](https://huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext)
+- Dictionary: [NCBI Taxonomy](https://www.ncbi.nlm.nih.gov/taxonomy) (See [FTP](https://ftp.ncbi.nih.gov/pub/taxonomy/new_taxdump/))
+
+This model variant does not perform abbreviation resolution via [A3bP](https://github.com/ncbi-nlp/Ab3P)
+
+### 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
+from flair.tokenization import SciSpacyTokenizer
+
+sentence = Sentence(
+    "The mutation in the ABCD1 gene causes X-linked adrenoleukodystrophy, "
+    "a neurodegenerative disease, which is exacerbated by exposure to high "
+    "levels of mercury in dolphin populations.",
+    use_tokenizer=SciSpacyTokenizer()
+)
+
+# load hunflair to detect the entity mentions we want to link.
+tagger = Classifier.load("hunflair-species")
+tagger.predict(sentence)
+
+# load the linker and dictionary
+linker = EntityMentionLinker.load("species-linker")
+linker.predict(sentence)
+
+# print the results for each entity mention:
+for span in sentence.get_spans(tagger.label_type):
+    for link in span.get_labels(linker.label_type):
+      print(f"{span.text} -> {link.value}")
+```
+
+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("cambridgeltl/SapBERT-from-PubMedBERT-fulltext", dictionary_name_or_path="ncbi-taxonomy", entity_type="species", hybrid_search=False)
+```
+
+This will reduce the download requirements, at the cost of computation. Note `hybrid_search=False` as SapBERT unlike BioSyn is trained only for dense retrieval.