Update README.md

README.md

@@ -313,7 +313,7 @@ An inverse trend can be observed, with SLIMER emerging as the most effective in
   <div class="template">
     <pre>{
   "description": "SLIMER prompt",
-  "prompt_input": "<|start_header_id|>system<|end_header_id|>You are given a text chunk (delimited by triple quotes) and an instruction.\nRead the text and answer to the instruction in the end.\n\"\"\"\n{<span class="highlight-orange">input</span>}\n\"\"\"\nInstruction: Extract the Named Entities of type {<span class="highlight-orange">NE_name</span>} from the text chunk you have read. You are given a DEFINITION and some GUIDELINES.\nDEFINITION: {<span class="highlight-orange">definition</span>}\nGUIDELINES: {<span class="highlight-orange">guidelines</span>}\nReturn a JSON list of instances of this Named Entity type (for example [\"text_span_1\", \"text_span_2\"]. Return an empty list [] if no instances are present. Return only the JSON list, no further motivations or introduction to the answer.<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n\n"
+  "prompt_input": "<|start_header_id|>system<|end_header_id|>\n\nYou are an expert in Named Entity Recognition designed to output JSON only.<|eot_id|>\n<|start_header_id|>user<|end_header_id|>\n\nYou are given a text chunk (delimited by triple quotes) and an instruction.\nRead the text and answer to the instruction in the end.\n\"\"\"\n{<span class="highlight-orange">input</span>}\n\"\"\"\nInstruction: Extract the Named Entities of type {<span class="highlight-orange">NE_name</span>} from the text chunk you have read. You are given a DEFINITION and some GUIDELINES.\nDEFINITION: {<span class="highlight-orange">definition</span>}\nGUIDELINES: {<span class="highlight-orange">guidelines</span>}\nReturn a JSON list of instances of this Named Entity type (for example [\"text_span_1\", \"text_span_2\"]. Return an empty list [] if no instances are present. Return only the JSON list, no further motivations or introduction to the answer.<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n\n"
 }</pre>
   </div>
 </body>
@@ -323,9 +323,9 @@ An inverse trend can be observed, with SLIMER emerging as the most effective in
 ```python
 from vllm import LLM, SamplingParams
 
-vllm_model = LLM(model="expertai/SLIMER")
+vllm_model = LLM(model="expertai/SLIMER-LLaMA3")
 
-sampling_params = SamplingParams(temperature=0, max_tokens=128, stop=['</s>'])
+sampling_params = SamplingParams(temperature=0, max_tokens=128)
 
 prompts = [prompter.generate_prompt(instruction, input) for instruction, input in instruction_input_pairs]
 responses = vllm_model.generate(prompts, sampling_params)