llmware
/

slim-sa-ner

Text Generation

Model card Files Files and versions

doberst commited on Mar 20, 2024

Commit

71d3bdb

·

verified ·

1 Parent(s): ebb116e

Update README.md

Files changed (1) hide show

README.md +2 -1

README.md CHANGED Viewed

@@ -9,7 +9,8 @@ inference: false
 **slim-sa-ner** combines two of the most popular traditional classifier functions (**Sentiment Analysis** and **Named Entity Recognition**), and reimagines them as function calls on a specialized decoder-based LLM, generating output consisting of a python dictionary with keys corresponding to sentiment, and NER identifiers, such as people, organization, and place, e.g.:
-&nbsp;&nbsp;&nbsp;&nbsp;`{'sentiment': ['positive'], people': ['..'], 'organization': ['..'],'place': ['..]}`
 This 'combo' model is designed to illustrate the potential power of using function calls on small, specialized models to enable a single model architecture to combine the capabilities of what were traditionally two separate model architectures on an encoder.

 **slim-sa-ner** combines two of the most popular traditional classifier functions (**Sentiment Analysis** and **Named Entity Recognition**), and reimagines them as function calls on a specialized decoder-based LLM, generating output consisting of a python dictionary with keys corresponding to sentiment, and NER identifiers, such as people, organization, and place, e.g.:
+&nbsp;&nbsp;&nbsp;&nbsp;`{'sentiment': ['positive'], people': ['..'], 'organization': ['..'],
+&nbsp;&nbsp;&nbsp;&nbsp; 'place': ['..]}`
 This 'combo' model is designed to illustrate the potential power of using function calls on small, specialized models to enable a single model architecture to combine the capabilities of what were traditionally two separate model architectures on an encoder.