can run in browser

README.md

@@ -18,7 +18,7 @@ yarn build
 
 ## Use
 
-To use this tool
+To use this tool in nodejs, you can use the following code:
 
 ```js
 
@@ -44,6 +44,29 @@ main()
 
 ```
 
+In the browser, you can use the following code:
+
+```js
+import { SentencePieceProcessor, cleanText, llama_3_1_tokeniser_b64 } from "@sctg/sentencepiece-js";
+// built in models: llama_3_1_tokeniser_b64, clean_30k_b64, smart_b64
+async function main() {
+
+    let text = "I am still waiting on my card?"
+    let cleaned = cleanText(text)
+
+    let spp = new SentencePieceProcessor()
+    await spp.lloadFromB64StringModel(llama_3_1_tokeniser_b64);
+    let ids = spp.encodeIds(cleaned)
+    console.log(ids)
+    let str = spp.decodeIds(ids) // list ids->number
+    console.log(str)
+
+    let pieces = spp.encodePieces(cleaned) // list tokens->string
+    console.log(pieces)
+}
+main()
+```
+
 ## Note
 
 - devilyouwei updated this repo to make this module support the js `require` keyword and added the using example.

package.json

@@ -1,6 +1,6 @@
 {
   "name": "@sctg/sentencepiece-js",
-  "version": "1.2.0",
+  "version": "1.3.0",
   "description": "Sentencepiece tokenization for natural language processing, JS version.",
   "main": "dist/index.js",
   "exports": {
@@ -11,12 +11,12 @@
     "build": "./build.sh; rollup --config",
     "test": "web-test-runner \"test/**/*.test.js\" \"src/**/*.test.js\" --node-resolve",
     "test:watch": "web-test-runner \"test/**/*.test.js\" \"src/**/*.test.js\" --node-resolve --watch",
-    "develop": "web-dev-server --node-resolve --watch --open"
+    "develop": "web-dev-server --node-resolve --watch --open",
+    "convert_models": "node ./test/convertmodels.js"
   },
   "files": [
     "./dist/index.js",
-    "./dist/index.d.ts",
-    "./dist/llama-3.1-tokenizer.model"
+    "./dist/index.d.ts"
   ],
   "repository": {
     "type": "git",

src/index.ts

@@ -1,4 +1,7 @@
 import { SentencePieceProcessor, cleanText } from "./sentencePieceProcessor";
+import { llama_3_1_tokeniser_b64 } from "./llama_3_1_tokeniser_model";
+import { smart_b64 } from "./smart";
+import { clean_30k_b64 } from "./clean_30k";
 
-export { SentencePieceProcessor, cleanText }
-export default { SentencePieceProcessor, cleanText }
+export { SentencePieceProcessor, cleanText, llama_3_1_tokeniser_b64, clean_30k_b64, smart_b64 };
+export default { SentencePieceProcessor, cleanText };

src/sentencePieceProcessor.ts

@@ -6,14 +6,34 @@ export class SentencePieceProcessor {
     processor: any;
     sentencepiece: any;
 
+    uuidv4(): string {
+        return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function (c) {
+            var r = Math.random() * 16 | 0,
+                v = c == 'x' ? r : (r & 0x3 | 0x8);
+            return v.toString(16);
+        });
+    }
+
+    // load model from a base64 encoded string
+    async loadFromB64StringModel(b64model: string) {
+        // decode base64 string 
+        const model = Buffer.from(b64model, 'base64');
+        await this._loadModel(model);
+    }
+
     // load model
     async load(url: string) {
+        const model = fs.readFileSync(url);
+        await this._loadModel(model);
+    }
 
-        this.sentencepiece = await Module();
 
-        // change to fs read model file
-        this.sentencepiece.FS.writeFile("sentencepiece.model", fs.readFileSync(url));
-        const string_view = new this.sentencepiece.StringView("sentencepiece.model");
+    // private function to load model
+    private async _loadModel(model: Buffer) {
+        const tempName = this.uuidv4() + ".model";
+        this.sentencepiece = await Module();
+        this.sentencepiece.FS.writeFile(tempName, model);
+        const string_view = new this.sentencepiece.StringView(tempName);
         const absl_string_view = string_view.getView();
 
         this.processor = new this.sentencepiece.SentencePieceProcessor();
@@ -22,10 +42,9 @@ export class SentencePieceProcessor {
         load_status.delete();
         absl_string_view.delete();
         string_view.delete();
-
+        this.sentencepiece.FS.unlink(tempName);
     }
 
-
     encodeIds(text: string) {
 
         const string_view = new this.sentencepiece.StringView(text);

src/test.js

@@ -1,4 +1,4 @@
-const { SentencePieceProcessor, cleanText } = require("../dist");
+const { SentencePieceProcessor, cleanText, llama_3_1_tokeniser_b64 } = require("../dist");
 const ROOT = require('app-root-path')
 
 async function main() {
@@ -10,10 +10,22 @@ async function main() {
     await spp.load(`${ROOT}/test/llama-3.1-tokenizer.model`)
     let ids = spp.encodeIds(cleaned);
     console.log(ids)
+    console.log(`Token length: ${ids.length}`)
     let str = spp.decodeIds(ids)
     console.log(str)
 
     let pieces = spp.encodePieces(cleaned);
     console.log(pieces)
+
+    let spp2 = new SentencePieceProcessor();
+    await spp2.loadFromB64StringModel(llama_3_1_tokeniser_b64);
+    let ids2 = spp2.encodeIds(cleaned);
+    console.log(ids2)
+    console.log(`Token length: ${ids2.length}`)
+    let str2 = spp2.decodeIds(ids2)
+    console.log(str2)
+
+    let pieces2 = spp2.encodePieces(cleaned);
+    console.log(pieces2);
 }
 main()

test/convertmodels.js

@@ -0,0 +1,33 @@
+const fs = require('fs');
+const path = require('path');
+
+function convertMoldel(filePath, output, variableName) {
+    fs.readFile(filePath, (err, data) => {
+        if (err) {
+            console.error('Erreur lors de la lecture du fichier binaire:', err);
+            return;
+        }
+
+        // Convertir le contenu en base64
+        const base64Content = data.toString('base64');
+
+        // Créer la chaîne de caractères contenant l'exportation de la constante
+        const outputContent = `export const ${variableName} = "${base64Content}";`;
+
+        // Chemin vers le fichier de sortie
+        const outputFilePath = path.join(__dirname, output);
+
+        // Écrire la chaîne dans le fichier de sortie
+        fs.writeFile(outputFilePath, outputContent, (err) => {
+            if (err) {
+                console.error('Erreur lors de l\'écriture du fichier de sortie:', err);
+                return;
+            }
+            console.log('Fichier de sortie généré avec succès:', outputFilePath);
+        });
+    });
+}
+
+convertMoldel('./test/llama-3.1-tokenizer.model', '../src/llama_3_1_tokeniser_model.ts', 'llama_3_1_tokeniser_b64');
+convertMoldel('./test/30k-clean.model', '../src/clean_30k.ts', 'clean_30k_b64');
+convertMoldel('./test/smart.model', '../src/smart.ts', 'smart_b64');