Update README.md

README.md

@@ -1,38 +1,28 @@
 ---
 license: apache-2.0
 inference: false 
-tags: [green, p1, llmware-fx, ov, emerald]
+tags: [green, p1, llmware-encoder, ov]
 ---
 
-# slim-extract-tiny-ov  
+# unitary-toxic-roberta-ov  
 
-**slim-extract-tiny-ov** is a specialized function calling model with a single mission to look for values in a text, based on an "extract" key that is passed as a parameter.  No other instructions are required except to pass the context passage, and the target key, and the model will generate a python dictionary consisting of the extract key and a list of the values found in the text, including an 'empty list' if the text does not provide an answer for the value of the selected key.  
-
-This is an OpenVino int4 quantized version of slim-extract-tiny, providing a very fast, very small inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU.    
+**unitary-toxic-roberta-ov** is a toxicity classifier from [unitary/unbiased-toxic-roberta](https://www.huggingface.com/unitary/unbiased-toxic-roberta), packaged in OpenVino format. 
 
+The classifier can be used to evaluate toxic content in a prompt or in model output.  
 
 ### Model Description
 
-- **Developed by:** llmware  
-- **Model type:** tinyllama  
-- **Parameters:** 1.1 billion
-- **Model Parent:** llmware/slim-extract-tiny
+- **Developed by:** unitary
+- **Quantized by:** llmware
+- **Model type:** roberta  
+- **Parameters:** 125 million
+- **Model Parent:** unitary/unbiased-toxic-roberta
 - **Language(s) (NLP):** English  
 - **License:** Apache 2.0  
-- **Uses:** Extraction of values from complex business documents    
+- **Uses:** Prompt safety    
 - **RAG Benchmark Accuracy Score:** NA  
 - **Quantization:** int4  
   
-### Example Usage  
-
-    from llmware.models import ModelCatalog
-
-    text_passage = "The company announced that for the current quarter the total revenue increased by 9% to $125 million."  
-    model = ModelCatalog().load_model("slim-extract-tiny-ov")   
-    llm_response = model.function_call(text_passage, function="extract", params=["revenue"])  
-
-    Output: `llm_response = {"revenue": [$125 million"]}`  
-  
 
 ## Model Card Contact