add to max tokens

sketch.js

@@ -10,11 +10,10 @@ env.allowLocalModels = false;
 var PREPROMPT = `Please continue the story, and fill any [MASK] with your own words:`
 // let PREPROMPT = `Please complete the phrase and fill in any [MASK]: `
 var PROMPT_INPUT = `The [BLANK] has a job as a [MASK] but...` // a field for writing or changing a text value
-var pField // an html element for the prompt
-var outText // an html element for the results
-var blanksArray = [] 
-// a list for all the variable inputs, e.g. ["woman", "man", "non-binary person"]
-
+var promptField // an html element to hold the prompt
+var outText // an html element to hold the results
+var blanksArray = [] // an empty list to store all the variables we enter to modify the prompt
+// e.g. ["woman", "man", "non-binary person"]
 
 // RUN TEXT-GEN MODEL
 
@@ -25,7 +24,7 @@ async function textGenTask(pre, prompt, blanks){
   let promptArray = []
   promptArray.push(pre) // add preprompt to the list of prompts
 
-  // fill in blanks from our sample prompt and make new prompts using our variable list 'blanksArray'
+  // Fill in blanks from our sample prompt and make new prompts using our variable list 'blanksArray'
   blanks.forEach(b => {
     let p = prompt.replace('[BLANK]', b) // replace the string segment with an item from the blanksArray
     promptArray.push(p) // add the new prompt to the list we created
@@ -54,27 +53,20 @@ async function textGenTask(pre, prompt, blanks){
   // const pipe = await pipeline('text-generation', MODEL) //different task type, also for text generation
   const pipe = await pipeline('text2text-generation', MODEL)
 
-  var hyperparameters = { max_new_tokens: 60, top_k: 90, repetition_penalty: 1.5 }
+  var hyperparameters = { max_new_tokens: 200, top_k: 90, repetition_penalty: 1.5 }
     // setting hyperparameters
-    // max_new_tokens: 256, top_k: 50, temperature: 0.7, do_sample: true, no_repeat_ngram_size: 2,
-    // , num_return_sequences: 2 (must be 1?)
-
-  // let OUTPUT_LIST = [] // a blank array to store the results from the model
+    // max_new_tokens: 256, top_k: 50, temperature: 0.7, do_sample: true, no_repeat_ngram_size: 2, num_return_sequences: 2 (must be 1?)
 
   // change model run to iterative for each prompt generated locally — will be more expensive??
-  // promptArray.forEach(async i => {
+  // promptArray.forEach(async i => {} //this was a loop to wrap model run multiple times
   
   // RUN INPUT THROUGH MODEL, 
-    var out = await pipe(INPUT, hyperparameters)
-  
-    console.log(await out)
-    console.log('text-gen task completed')
-  
-    // PARSE RESULTS as a list of outputs, two different ways depending on the model
+  var out = await pipe(INPUT, hyperparameters)
 
-    // OUTPUT_LIST.push(out[0].generated_text)
-
-  // }) //this was a loop to wrap model run multiple times
+  console.log(await out)
+  console.log('text-gen task completed')
+  
+  // PARSE RESULTS as a list of outputs, two different ways depending on the model
   
   // parsing of output
   // await out.forEach(o => {
@@ -89,6 +81,7 @@ async function textGenTask(pre, prompt, blanks){
   // })
 
   let OUTPUT_LIST = out[0].generated_text //not a list anymore just one result
+  // OUTPUT_LIST.push(out[0].generated_text)
 
   console.log(OUTPUT_LIST)
   console.log('text-gen parsing complete')
@@ -147,11 +140,11 @@ new p5(function (p5){
   }
 
   function makeFields(){
-    pField = p5.createInput(PROMPT_INPUT) // turns the string into an input; now access the text via PROMPT_INPUT.value()
-    pField.size(700)
-    pField.attribute('label', `Write a text prompt with one [MASK] that the model will fill in.`)
-    p5.createP(pField.attribute('label'))
-    pField.addClass("prompt")
+    promptField = p5.createInput(PROMPT_INPUT) // turns the string into an input; now access the text via PROMPT_INPUT.value()
+    promptField.size(700)
+    promptField.attribute('label', `Write a text prompt with one [MASK] that the model will fill in.`)
+    p5.createP(promptField.attribute('label'))
+    promptField.addClass("prompt")
 
     const fieldsDiv = p5.createDiv()
     fieldsDiv.id('fieldsDiv')
@@ -183,21 +176,19 @@ new p5(function (p5){
     // insert waiting dots into results space of interface
     outText.html('...', false)
 
-    PROMPT_INPUT = pField.value() // grab update to the prompt if it's been changed
+    // GRAB CURRENT FIELD INPUTS FROM PROMPT & BLANKS
+    PROMPT_INPUT = promptField.value() // grab update to the prompt if it's been changed
     console.log("latest prompt: ", PROMPT_INPUT)
 
-    // let blanksValues = blanksArray.map(b => b.value)
     let blanksValues = []
     blanksArray.forEach(b => {
       blanksValues.push(b.value())
     })
     console.log(blanksValues)
-    
-    // blanksArray.forEach(b => console.log(b.value()))
+    // let blanksValues = blanksArray.map(b => b.value)    
     
     // call the function that runs the model for the task of your choice here 
     // make sure to use the PROMPT_INPUT as a parameter, or also the PREPROMPT if valid for that task
-    
     let outs = await textGenTask(PREPROMPT, PROMPT_INPUT, blanksValues)
     console.log(outs)