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Modified NER prompt for keyword position [WIP] #25

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Modified NER prompt for keyword position [WIP] #25

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7 changes: 5 additions & 2 deletions config/ner.js
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
export const ner = ({
text_input = "",
description = "",
position,
domain = "",
labels = "",
examples = [],
Expand All @@ -14,13 +15,15 @@ export const ner = ({
const _example =
examples && examples.length > 0
? `Examples: ${examples.map(
(ex) => `Input ${ex.sentence} Output [${ex.sentence}]`
(ex) => `Input ${ex[0].E} Output [${ex[0].T}]`
)}`
: "";
return `
${description}
You are a highly intelligent and accurate ${_domain} Named-entity recognition(NER) system. You take Passage as input and your task is to recognize and extract specific types of ${_domain} named entities in that given passage and classify into a set of ${_label}.
Your output format is only {{ output_format|default("[{{'T': type of entity from predefined entity types, 'E': entity in the input text}},...,{{'branch' : Appropriate branch of the passage ,'group': Appropriate Group of the passage}}]") }} form, no other form.

Your output format is ${position ? `{{ output_format|default("[{{'T': type of entity from predefined entity types, 'E': entity in the input text}},...,{{'branch' : Appropriate branch of the passage ,'group': Appropriate Group of the passage}},...,{{ 'start' : start position of the entity, 'end' : end position of the entity}}]") }}` : `{{ output_format|default("[{{'T': type of entity from predefined entity types, 'E': entity in the input text}},...,{{'branch' : Appropriate branch of the passage ,'group': Appropriate Group of the passage}}]") }}`} form, otherwise no other form.

${_example}
Input: ${text_input}
Output:
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4 changes: 4 additions & 0 deletions examples/data/optimized_ner.js
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Original file line number Diff line number Diff line change
Expand Up @@ -5,10 +5,14 @@ export const nerData = {
{
E: "DISEASE",
T: "Alzheimer's disease",
start: 0,
end: 10
},
{
E: "BIOMARKER",
T: "Amyloid β42",
start: 5,
end: 16
},
{
E: "BIOMARKER",
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5 changes: 4 additions & 1 deletion examples/nerExample.js
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Expand Up @@ -3,7 +3,7 @@ import { OpenAI } from "../models/openai.js";
import { Prompter } from "../promptify/index.js";
import { nerData } from "../examples/data/optimized_ner.js";

const model = OpenAI("api-key");
const model = OpenAI("openai-api");
const examples = nerData.samples[0].data;
const firstExample = examples.slice(0, 3);

Expand All @@ -12,9 +12,12 @@ const prompt = ner({
description: "Medicine NER Expert",
domain: "medicine",
labels: "",
position: true,
examples: [firstExample],
});

console.log(prompt)

const result = await Prompter(model, prompt, "text-davinci-003");

console.log(result);