try fillMask version test

README.md

@@ -11,9 +11,10 @@ hf_oauth_scopes:
   - write-repos
   - inference-api
 models:
+- bert-base-uncased
 # - gpt-3.5-turbo
-- bigscience/bloom-560m
-- Xenova/distilgpt2
+# - bigscience/bloom-560m
+# - Xenova/distilgpt2
 # - HuggingFaceH4/zephyr-7b-gemma-v0.1
 # - HuggingFaceH4/zephyr-7b-beta
 # - openai-community/gpt2

sketch.js

@@ -1,10 +1,10 @@
 // connect to API via module
 
 // import { AutoTokenizer, env } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers';
-// import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers';
+import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers';
 
 /// AUTHORIZATION
-import { textGeneration } from 'https://esm.sh/@huggingface/inference';
+// import { textGeneration } from 'https://esm.sh/@huggingface/inference';
 import { oauthLoginUrl, oauthHandleRedirectIfPresent } from 'https://esm.sh/@huggingface/hub';
 
 const oauthResult = await oauthHandleRedirectIfPresent();
@@ -34,6 +34,7 @@ console.log(HF_TOKEN)
 // establish global variables to reference later
 var promptInput
 var blanksArray = []
+var resultsArray = []
 
 // pick a model (see list of models)
 // INFERENCE MODELS
@@ -140,6 +141,29 @@ new p5(function (p5) {
         }
     }
 
+    // async function getInputs(){
+    //     // Map the list of blanks text values to a new list
+    //     let BLANKSVALUES = blanksArray.map(i => i.value())
+    //     console.log(BLANKSVALUES)
+        
+    //     // Do model stuff in this function instead of in general
+    //     let PROMPT = promptInput.value() // updated check of the prompt field
+        
+    //     // BLANKS = inputValues // get ready to feed array list into model
+
+    //     let PREPROMPT = `In the sentence I provide, please fill in the [BLANK] with each word in the array ${BLANKSVALUES}, replace any [MASK] with a word of your choice. Here is the SAMPLE SENTENCE: ` 
+
+    //     // we pass PROMPT and PREPROMPT to the model function, don't need to pass BLANKSVALUES bc it's passed into the PREPROMPT already here
+
+    //     // Please return an array of sentences based on the sample sentence to follow. In each sentence, 
+
+    //     // let modelResult = await runModel(PREPROMPT, PROMPT)
+    //     let modelResult = await runModel(BLANKSVALUES, PROMPT)
+
+    //     await displayModel(modelResult)
+    // }
+
+    // creating multiple prompt inputs rather than instructing model to do so
     async function getInputs(){
         // Map the list of blanks text values to a new list
         let BLANKSVALUES = blanksArray.map(i => i.value())
@@ -150,15 +174,25 @@ new p5(function (p5) {
         
         // BLANKS = inputValues // get ready to feed array list into model
 
-        let PREPROMPT = `In the sentence I provide, please fill in the [BLANK] with each word in the array ${BLANKSVALUES}, replace any [FILL] with a word of your choice. Here is the SAMPLE SENTENCE: ` 
+        let PROMPTS = []
+        for (let b in BLANKSVALUES){
+            console.log(b)
+            p = PROMPT.replace('[BLANK]', `${b}`)
+            console.log(p)
+            PROMPTS.add(p)
+        }
+        console.log(PROMPTS)
+
+        // let PREPROMPT = `In the sentence I provide, please fill in the [BLANK] with each word in the array ${BLANKSVALUES}, replace any [MASK] with a word of your choice. Here is the SAMPLE SENTENCE: ` 
 
         // we pass PROMPT and PREPROMPT to the model function, don't need to pass BLANKSVALUES bc it's passed into the PREPROMPT already here
 
         // Please return an array of sentences based on the sample sentence to follow. In each sentence, 
 
-        let modelResult = await runModel(PREPROMPT, PROMPT)
+        // let modelResult = await runModel(PREPROMPT, PROMPT)
+        let modelResult = await runModel(PROMPTS)
 
-        await displayModel(modelResult)
+        await displayModel(resultsArray[0], resultsArray[1])
     }
 
     async function displayModel(m){
@@ -171,61 +205,61 @@ new p5(function (p5) {
 ///// MODEL STUFF
 
 
-async function runModel(PREPROMPT, PROMPT){
-    // inference API version
+// async function runModel(PREPROMPT, PROMPT){
+//     // inference API version
 
-    let INPUT = PREPROMPT + PROMPT
+//     let INPUT = PREPROMPT + PROMPT
 
-    // let MODELNAME = "HuggingFaceH4/zephyr-7b-beta"
-    // let MODELNAME = "openai-community/gpt2"
-    // let MODELNAME = 'mistral_inference'
-    // let MODELNAME = 'Xenova/distilgpt2'
-    let MODELNAME = 'bigscience/bloom-560m'
+//     // let MODELNAME = "HuggingFaceH4/zephyr-7b-beta"
+//     // let MODELNAME = "openai-community/gpt2"
+//     // let MODELNAME = 'mistral_inference'
+//     // let MODELNAME = 'Xenova/distilgpt2'
+//     let MODELNAME = 'bigscience/bloom-560m'
 
-    let out = await textGeneration({    
-        accessToken: HF_TOKEN,
-        model: MODELNAME,
-        inputs: INPUT,
-        parameters: {
-            max_new_tokens: 128
-        }
-    });
-
-    // let out = await inference.textGeneration(INPUT, {
-    //     model: MODELNAME,
-    //     max_new_tokens: 128
-    // })
-
-    // let out = await inference.textGeneration(INPUT, 'bigscience/bloom-560m')
-
-    // text-generation-inference
-    // Uncaught (in promise) Error: HfApiJson(Deserialize(Error("unknown variant `transformers.js`, expected one of `text-generation-inference`, `transformers`, `allennlp`, `flair`, `espnet`, `asteroid`, `speechbrain`, `timm`, `sentence-transformers`, `spacy`, `sklearn`, `stanza`, `adapter-transformers`, `fasttext`, `fairseq`, `pyannote-audio`, `doctr`, `nemo`, `fastai`, `k2`, `diffusers`, `paddlenlp`, `mindspore`, `open_clip`, `span-marker`, `bertopic`, `peft`, `setfit`", line: 1, column: 397)))
-
-    // let out = await inference.textGeneration({    
-    //     accessToken: HF_TOKEN,
-    //     model: MODELNAME,
-    //     messages: [{ 
-    //                 role: "system", 
-    //                 content: PREPROMPT
-    //             },{
-    //                 role: "user",
-    //                 content: PROMPT
-    //             }],
-    //     max_new_tokens: 128
-    // });
+//     let out = await textGeneration({    
+//         accessToken: HF_TOKEN,
+//         model: MODELNAME,
+//         inputs: INPUT,
+//         parameters: {
+//             max_new_tokens: 128
+//         }
+//     });
 
-    console.log(out)
+//     // let out = await inference.textGeneration(INPUT, {
+//     //     model: MODELNAME,
+//     //     max_new_tokens: 128
+//     // })
 
-    console.log(out.token.text, out.generated_text)
+//     // let out = await inference.textGeneration(INPUT, 'bigscience/bloom-560m')
 
-    // modelResult = await out.messages[0].content
+//     // text-generation-inference
+//     // Uncaught (in promise) Error: HfApiJson(Deserialize(Error("unknown variant `transformers.js`, expected one of `text-generation-inference`, `transformers`, `allennlp`, `flair`, `espnet`, `asteroid`, `speechbrain`, `timm`, `sentence-transformers`, `spacy`, `sklearn`, `stanza`, `adapter-transformers`, `fasttext`, `fairseq`, `pyannote-audio`, `doctr`, `nemo`, `fastai`, `k2`, `diffusers`, `paddlenlp`, `mindspore`, `open_clip`, `span-marker`, `bertopic`, `peft`, `setfit`", line: 1, column: 397)))
 
-    // var modelResult = await out.choices[0].message.content
-    var modelResult = await out[0].generated_text
-    console.log(modelResult);
+//     // let out = await inference.textGeneration({    
+//     //     accessToken: HF_TOKEN,
+//     //     model: MODELNAME,
+//     //     messages: [{ 
+//     //                 role: "system", 
+//     //                 content: PREPROMPT
+//     //             },{
+//     //                 role: "user",
+//     //                 content: PROMPT
+//     //             }],
+//     //     max_new_tokens: 128
+//     // });
 
-    return modelResult
-}
+//     console.log(out)
+
+//     console.log(out.token.text, out.generated_text)
+
+//     // modelResult = await out.messages[0].content
+
+//     // var modelResult = await out.choices[0].message.content
+//     var modelResult = await out[0].generated_text
+//     console.log(modelResult);
+
+//     return modelResult
+// }
 
 
     //inference.fill_mask({
@@ -309,4 +343,23 @@ async function runModel(PREPROMPT, PROMPT){
 
 //     return modelResult
 
-// }
+// }
+
+
+async function runModel(PROMPTS){
+
+    let MODELNAME = "bert-base-uncased"
+
+    let unmasker = await pipeline('fill-mask', 'bert-base-uncased')
+
+    for (let p in PROMPTS){
+        var res = unmasker(p)
+        console.log(res)
+
+        var modelResult = res[0].token_str
+        console.log(modelResult)
+
+        resultsArray.add(modelResult)
+    }
+    return resultsArray
+}