Update README.md

README.md

@@ -3,28 +3,31 @@ license: cc-by-sa-4.0
 inference: false
 ---
 
-# SLIM-SA-NER-3B
+# SLIM-TAGS-3B
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-**slim-sa-ner-3b** 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.:  
+**slim-tags-3b** is a small, specialized function-calling model fine-tuned to extract and generate meaningful tags from a chunk of text.  
 
-&nbsp;&nbsp;&nbsp;&nbsp;`{'sentiment': ['positive'], people': ['..'], 'organization': ['..'],'place': ['..]}`
+Tags generally correspond with a 'wide' interpretation of named entities, but will also include key objects, entities and phrases that contribute meaningfully to the semantic meaning of the text.  
 
-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.  
+The model is invoked as a specialized 'tags' classifier function that outputs a python dictionary in the form of:  
 
-The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs, providing an intuitive, flexible natural language response, without complex prompting, and with improved generalization and ability to fine-tune to a specific domain use case.  
+&nbsp;&nbsp;&nbsp;&nbsp;`{'tags': ['NASDAQ', 'S&P', 'Dow', 'Verizon', 'Netflix, ... ']}`  
 
+with the value items in the list generally being extracted from the source text.  
+
+The intended use of the model is to auto-generate tags to text that can be used to enhance search retrieval, categorization, or to extract named entities that can be used programmatically in follow-up queries or prompts.  It can also be used for fact-checking as a secondary validation on a longer (separate) LLM output.  
 
 This model is fine-tuned on top of [**llmware/bling-stable-lm-3b-4e1t-v0**](https://huggingface.co/llmware/bling-stable-lm-3b-4e1t-v0), which in turn, is a fine-tune of stabilityai/stablelm-3b-4elt.
 
-Each slim model has a 'quantized tool' version, e.g.,  [**'slim-sa-ner-3b-tool'**](https://huggingface.co/llmware/slim-sa-ner-3b-tool).  
+Each slim model has a 'quantized tool' version, e.g.,  [**'slim-tags-3b-tool'**](https://huggingface.co/llmware/slim-tags-3b-tool).  
 
 
 ## Prompt format:
 
 `function = "classify"`  
-`params = "sentiment, person, organization, place"`  
+`params = "tags"`  
 `prompt = "<human> " + {text} + "\n" + `  
 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp;`"<{function}> " + {params} + "</{function}>" + "\n<bot>:"`  
 
@@ -32,8 +35,8 @@ Each slim model has a 'quantized tool' version, e.g.,  [**'slim-sa-ner-3b-tool'*
 <details>
 <summary>Transformers Script </summary>
 
-    model = AutoModelForCausalLM.from_pretrained("llmware/slim-sa-ner-3b")
-    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sa-ner-3b")
+    model = AutoModelForCausalLM.from_pretrained("llmware/slim-tags-3b")
+    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-tags-3b")
 
     function = "classify"
     params = "topic"
@@ -75,8 +78,8 @@ Each slim model has a 'quantized tool' version, e.g.,  [**'slim-sa-ner-3b-tool'*
 <summary>Using as Function Call in LLMWare</summary>
 
     from llmware.models import ModelCatalog
-    slim_model = ModelCatalog().load_model("llmware/slim-sa-ner-3b")
-    response = slim_model.function_call(text,params=["sentiment", "people", "organization", "place"], function="classify")
+    slim_model = ModelCatalog().load_model("llmware/slim-tags-3b")
+    response = slim_model.function_call(text,params=["tags"], function="classify")
 
     print("llmware - llm_response: ", response)