Add OCR, add configuration

server/events/trainAI.js

@@ -1,51 +1,53 @@
 import FastText from 'fasttext.js';
-const ft = new FastText({
-    train: {
-        // number of concurrent threads
-        thread: 8,
-        // verbosity level [2]
-        verbose: 4,
-        // number of negatives sampled [5]
-        neg: 7,
-        // loss function {ns, hs, softmax} [ns]
-        loss: 'ns',
-        // learning rate [0.05]
-        lr: 1,
-        // change the rate of updates for the learning rate [100]
-        lrUpdateRate: 1000,
-        // max length of word ngram [1]
-        wordNgrams: 5,
-        // minimal number of word occurences
-        minCount: 1,
-        // minimal number of word occurences
-        minCountLabel: 1,
-        // size of word vectors [100]
-        dim: 100,
-        // size of the context window [5]
-        ws: 5,
-        //  number of epochs [5]
-        epoch: 20,
-        // number of buckets [2000000]
-        bucket: 2000000,
-        // min length of char ngram [3]
-        minn: process.env.TRAIN_MINN || 3,
-        // max length of char ngram [6]
-        maxn: process.env.TRAIN_MAXN || 6,
-        // sampling threshold [0.0001]
-        t: 0.0001,
-        // load pre trained word vectors from unsupervised model
-        pretrainedVectors: ''
-    },
-    serializeTo: '/workspaces/revanced-helper/server/model/model',
-    trainFile: '/workspaces/revanced-helper/server/model/train.tsv',
-});
+import { join } from 'node:path';
 
-export default async function trainAI(unload, load) {
-    //unload();
+export default async function trainAI() {
+    const ft = new FastText({
+        train: {
+            // number of concurrent threads
+            thread: 8,
+            // verbosity level [2]
+            verbose: 4,
+            // number of negatives sampled [5]
+            neg: 7,
+            // loss function {ns, hs, softmax} [ns]
+            loss: 'ns',
+            // learning rate [0.05]
+            lr: 1,
+            // change the rate of updates for the learning rate [100]
+            lrUpdateRate: 1000,
+            // max length of word ngram [1]
+            wordNgrams: 5,
+            // minimal number of word occurences
+            minCount: 1,
+            // minimal number of word occurences
+            minCountLabel: 1,
+            // size of word vectors [100]
+            dim: 100,
+            // size of the context window [5]
+            ws: 5,
+            //  number of epochs [5]
+            epoch: 20,
+            // number of buckets [2000000]
+            bucket: 2000000,
+            // min length of char ngram [3]
+            minn: process.env.TRAIN_MINN || 3,
+            // max length of char ngram [6]
+            maxn: process.env.TRAIN_MAXN || 6,
+            // sampling threshold [0.0001]
+            t: 0.0001,
+            // load pre trained word vectors from unsupervised model
+            pretrainedVectors: ''
+        },
+        serializeTo: join(global.__dirname, global.config.fasttext.loadModel).replace('.bin', ''),
+        trainFile: join(global.__dirname, global.config.fasttext.trainFile),
+    });
+    
+    global.ft.unload();
 
     await ft.train()
 
-  //  load();
+    global.ft.load();
 
     return;
 }