Update README.md

README.md

@@ -7,22 +7,22 @@ inference: false
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-**slim-sentiment** is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") model series, consisting of small, specialized decoder-based models, fine-tuned for function-calling.  
+**slim-ratings** is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") model series, consisting of small, specialized decoder-based models, fine-tuned for function-calling.  
 
-slim-sentiment has been fine-tuned for **sentiment analysis** function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:  
+slim-ratings has been fine-tuned for **rating/stars** (sentiment degree) function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:  
 
-&nbsp;&nbsp;&nbsp;&nbsp;`{"sentiment": ["positive"]}`
+&nbsp;&nbsp;&nbsp;&nbsp;`{"rating": ["{rating score of 1(low) - 5(high)"]}` 
 
 
 SLIM models are designed to provide a flexible natural language generative model that can be used as part of a multi-step, multi-model LLM-based automation workflow.  
 
-Each slim model has a 'quantized tool' version, e.g.,  [**'slim-sentiment-tool'**](https://huggingface.co/llmware/slim-sentiment-tool).  
+Each slim model has a 'quantized tool' version, e.g.,  [**'slim-ratings-tool'**](https://huggingface.co/llmware/slim-ratings-tool).  
 
 
 ## Prompt format:
 
 `function = "classify"`  
-`params = "sentiment"`  
+`params = "rating"`  
 `prompt = "<human> " + {text} + "\n" + `  
 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp;`"<{function}> " + {params} + "</{function}>" + "\n<bot>:"`  
 
@@ -30,13 +30,13 @@ Each slim model has a 'quantized tool' version, e.g.,  [**'slim-sentiment-tool'*
 <details>
 <summary>Transformers Script </summary>
 
-    model = AutoModelForCausalLM.from_pretrained("llmware/slim-sentiment")
-    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sentiment")
+    model = AutoModelForCausalLM.from_pretrained("llmware/slim-ratings")
+    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-ratings")
 
     function = "classify"
-    params = "sentiment"
+    params = "rating"
 
-    text = "The stock market declined yesterday as investors worried increasingly about the slowing economy."  
+    text = "I am extremely impressed with the quality of earnings and growth that we have seen from the company this quarter."  
     
     prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:"
 
@@ -73,8 +73,8 @@ Each slim model has a 'quantized tool' version, e.g.,  [**'slim-sentiment-tool'*
 <summary>Using as Function Call in LLMWare</summary>
 
     from llmware.models import ModelCatalog
-    slim_model = ModelCatalog().load_model("llmware/slim-sentiment")
-    response = slim_model.function_call(text,params=["sentiment"], function="classify")
+    slim_model = ModelCatalog().load_model("llmware/slim-ratings")
+    response = slim_model.function_call(text,params=["rating"], function="classify")
 
     print("llmware - llm_response: ", response)