Tokenizer in JS is working

README.md

@@ -0,0 +1,66 @@
+---
+language:
+- en
+- ar
+- bg
+- ca
+- cs
+- da
+- de
+- el
+- es
+- et
+- fa
+- fi
+- fr
+- gl
+- gu
+- he
+- hi
+- hu
+- hy
+- id
+- it
+- ja
+- ka
+- ko
+- ku
+- lt
+- lv
+- mk
+- mn
+- mr
+- ms
+- my
+- nb
+- nl
+- pl
+- pt
+- ro
+- ru
+- sk
+- sl
+- sq
+- sr
+- sv
+- th
+- tr
+- uk
+- ur
+- vi
+- zh
+- hr
+license: apache-2.0
+---
+# Static Embeddings
+
+This project contains multilingual static embeddings that are appropriate for generating
+quick embeddings in edge devices. They are re-packaged from other projects in production
+ready assets.
+
+## Current model
+
+The current model is using [sentence-transformers/static-similarity-mrl-multilingual-v1](https://huggingface.co/sentence-transformers/static-similarity-mrl-multilingual-v1)
+as it is multi-lingual and uses Matryoshka Loss, which allows for the arbitrary truncation
+of the embedding vectors. The length of the vector can be tuned for its use, and still
+retain the semantic meaning.

build_models.py

@@ -0,0 +1,68 @@
+import numpy as np
+from zstandard import ZstdCompressor
+from pathlib import Path
+import io
+from sentence_transformers import SentenceTransformer
+from torch.nn import EmbeddingBag
+import torch
+
+
+def save_data(path: Path, tensor: torch.Tensor):
+    """Writes out the static embeddings to a .npy.zst file"""
+    assert str(path).endswith(".npy.zst")
+    buffer = io.BytesIO()
+    np.save(buffer, tensor.numpy())
+
+    with (
+        open(path, "wb") as outfile,
+        ZstdCompressor().stream_writer(outfile) as writer,
+    ):
+        writer.write(buffer.getvalue())
+
+
+data_path = Path("embeddings")
+
+model_name = "sentence-transformers/static-similarity-mrl-multilingual-v1"
+vocab_size = 105_879
+dimensions = 1024
+
+
+def load_embeddings():
+    model = SentenceTransformer(model_name)
+    embedding_bag: EmbeddingBag = model[0].embedding  # type: ignore
+    embeddings = torch.Tensor(embedding_bag.weight)
+
+    print(embeddings.shape)
+    assert embeddings.shape == torch.Size([vocab_size, dimensions])
+
+    print("float32")
+    print(f"  1024 dim - {embeddings.shape[0] * 1024 * 4 / 1024 / 1024:,.1f} MiB")
+    print(f"   512 dim - {embeddings.shape[0] * 512 * 4 / 1024 / 1024:,.1f} MiB")
+    print(f"   256 dim - {embeddings.shape[0] * 256 * 4 / 1024 / 1024:,.1f} MiB")
+
+    print("float16")
+    print(f"  1024 dim - {embeddings.shape[0] * 1024 * 2 / 1024 / 1024:,.1f} MiB")
+    print(f"   512 dim - {embeddings.shape[0] * 512 * 2 / 1024 / 1024:,.1f} MiB")
+    print(f"   256 dim - {embeddings.shape[0] * 256 * 2 / 1024 / 1024:,.1f} MiB")
+
+    for dim in (1024, 512, 384, 256, 128):
+        truncated = embeddings[:, :dim]
+        assert truncated.shape == torch.Size([vocab_size, dim])
+
+        save_data(data_path / f"static-embeddings.{dim}.fp32.npy.zst", embeddings)
+        save_data(
+            data_path / f"static-embeddings.{dim}.fp16.npy.zst",
+            embeddings.to(dtype=torch.float16),
+        )
+        save_data(
+            data_path / f"static-embeddings.{dim}.int8.npy.zst",
+            embeddings.to(dtype=torch.int8),
+        )
+
+
+def main() -> None:
+    load_embeddings()
+
+
+if __name__ == "__main__":
+    main()

js/example.mjs

@@ -0,0 +1,77 @@
+import { pipeline, AutoTokenizer, AutoModel, TokenizerModel } from '@huggingface/transformers';
+import fs from 'node:fs/promises';
+import { constants } from 'node:fs';
+import path from 'path';
+import { fileURLToPath } from 'url';
+
+const DIR = path.dirname(fileURLToPath(import.meta.url));
+
+await main()
+
+async function main() {
+  const url = "https://huggingface.co/sentence-transformers/static-similarity-mrl-multilingual-v1/resolve/main/0_StaticEmbedding/tokenizer.json"
+
+  const config = await ensureTokenizerJson(url)
+  // const tokenizer = TokenizerModel.fromConfig(config.model)
+  
+  const tokenizer = await AutoTokenizer.from_pretrained("./")
+  
+  const examples = [
+    "This is an example of encoding",
+    "The quick brown fox jumps over the lazy dog.",
+    "Curaçao, naïve fiancé, jalapeño, déjà vu.",
+    "Привет, как дела?",
+    "Бързата кафява лисица прескача мързеливото куче.",
+    "Γρήγορη καφέ αλεπού πηδάει πάνω από τον τεμπέλη σκύλο.",
+    "اللغة العربية جميلة وغنية بالتاريخ.",
+    "مرحبا بالعالم!",
+    "Simplified: 快速的棕色狐狸跳过懒狗。",
+    "Traditional: 快速的棕色狐狸跳過懶狗。",
+    "素早い茶色の狐が怠け者の犬を飛び越える。",
+    "コンピュータープログラミング",
+    "빠른 갈색 여우가 게으른 개를 뛰어넘습니다.",
+    "तेज़ भूरी लोमड़ी आलसी कुत्ते के ऊपर कूदती है।",
+    "দ্রুত বাদামী শিয়াল অলস কুকুরের উপর দিয়ে লাফ দেয়।",
+    "வேகமான பழுப்பு நரி சோம்பேறி நாயின் மேல் குதிக்கிறது.",
+    "สุนัขจิ้งจอกสีน้ำตาลกระโดดข้ามสุนัขขี้เกียจ.",
+    "ብሩክ ቡናማ ቀበሮ ሰነፍ ውሻን ተዘልሏል።",
+    // Mixed scripts:
+    "Hello 世界 مرحبا 🌍",
+    "123, αβγ, абв, العربية, 中文, हिन्दी.",
+  ];
+  for (const example of examples) {
+    console.log(tokenizer.tokenize(example))
+  }
+}
+
+/**
+ * @param {string} path
+ * @returns {Promise<string>}
+ */
+async function loadJSON(path) {
+  return JSON.parse(await fs.readFile(path, { encoding: 'utf8' }));
+}
+
+/**
+ * Download tokenizer.json if it does not already exist.
+ *
+ * @param {string} url - The URL to download tokenizer.json from
+ * @returns {Promise<any>} - Path to tokenizer.json
+ */
+export async function ensureTokenizerJson(url) {
+  const tokenizerPath = path.join(DIR, 'tokenizer.json');
+
+  try {
+    await fs.access(tokenizerPath, constants.F_OK);
+    console.log('Using', tokenizerPath);
+    return loadJSON(tokenizerPath);
+  } catch {}
+  
+  console.log("Downloading", url);
+  const response = await fetch(url);
+  const data = Buffer.from(await response.arrayBuffer());
+  await fs.writeFile(tokenizerPath, data);
+  
+  return loadJSON(tokenizerPath);
+
+}

js/package.json

@@ -0,0 +1,15 @@
+{
+  "name": "js",
+  "version": "1.0.0",
+  "description": "",
+  "main": "index.js",
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1"
+  },
+  "keywords": [],
+  "author": "",
+  "license": "ISC",
+  "dependencies": {
+    "@huggingface/transformers": "^3.7.2"
+  }
+}

js/tsconfig.json

@@ -0,0 +1,23 @@
+{
+  "compilerOptions": {
+    "module": "ESNext",
+    "moduleResolution": "nodenext",
+    // Set the baseUrl to the root of the project.
+    "baseUrl": "src",
+    // Make the type checking as strict as possible.
+    "strict": true,
+    // TypeScript will check JS files only if they have a @ts-check comment in them.
+    "allowJs": true,
+    "checkJs": true,
+    // Only type check, don't emit files.
+    "noEmit": true,
+    // Allow esnext syntax. Otherwise the default is ES5 only.
+    "target": "esnext",
+    "lib": ["esnext", "dom"],
+    "esModuleInterop": true
+  },
+  // Add a @ts-check comment to a JS file to start type checking it.
+  "include": ["example.mjs"],
+  // "files": ["src/@types/globals.d.ts"],
+  "exclude": []
+}

multilingual.py

@@ -1,10 +1,49 @@
 from sentence_transformers import SentenceTransformer
+from tokenizers import Encoding, Tokenizer
 from torch.nn import EmbeddingBag
 import torch
 
+examples = [
+    "This is an example of encoding",
+    "The quick brown fox jumps over the lazy dog.",
+    "Curaçao, naïve fiancé, jalapeño, déjà vu.",
+    "Привет, как дела?",
+    "Бързата кафява лисица прескача мързеливото куче.",
+    "Γρήγορη καφέ αλεπού πηδάει πάνω από τον τεμπέλη σκύλο.",
+    "اللغة العربية جميلة وغنية بالتاريخ.",
+    "مرحبا بالعالم!",
+    "Simplified: 快速的棕色狐狸跳过懒狗。",
+    "Traditional: 快速的棕色狐狸跳過懶狗。",
+    "素早い茶色の狐が怠け者の犬を飛び越える。",
+    "コンピュータープログラミング",
+    "빠른 갈색 여우가 게으른 개를 뛰어넘습니다.",
+    "तेज़ भूरी लोमड़ी आलसी कुत्ते के ऊपर कूदती है।",
+    "দ্রুত বাদামী শিয়াল অলস কুকুরের উপর দিয়ে লাফ দেয়।",
+    "வேகமான பழுப்பு நரி சோம்பேறி நாயின் மேல் குதிக்கிறது.",
+    "สุนัขจิ้งจอกสีน้ำตาลกระโดดข้ามสุนัขขี้เกียจ.",
+    "ብሩክ ቡናማ ቀበሮ ሰነፍ ውሻን ተዘልሏል።",
+    "Hello 世界 مرحبا 🌍",
+    "123, αβγ, абв, العربية, 中文, हिन्दी.",
+]
+
+tokenizer: Tokenizer = Tokenizer.from_file("js/tokenizer.json")
+
+for example in examples:
+    encoding: Encoding = tokenizer.encode(example)
+    tokenizer.decode
+    print(example)
+    print(encoding.tokens)
+    print()
+
+
+print("!!! tokenizer", tokenizer)
+assert False
+# https://huggingface.co/sentence-transformers/static-similarity-mrl-multilingual-v1
 model = SentenceTransformer(
-    "sentence-transformers/static-similarity-mrl-multilingual-v1"
+    "sentence-transformers/static-similarity-mrl-multilingual-v1", device="cpu"
 )
+embeddings = model.encode(examples)
+
 embedding_bag: EmbeddingBag = model[0].embedding  # type: ignore
 embeddings = torch.Tensor(embedding_bag.weight)
 

pyproject.toml

@@ -6,5 +6,7 @@ readme = "README.md"
 requires-python = ">=3.13"
 dependencies = [
     "model2vec>=0.6.0",
+    "numpy>=2.3.2",
     "sentence-transformers>=5.1.0",
+    "zstandard>=0.24.0",
 ]

uv.lock

@@ -650,13 +650,17 @@ version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
     { name = "model2vec" },
+    { name = "numpy" },
     { name = "sentence-transformers" },
+    { name = "zstandard" },
 ]
 
 [package.metadata]
 requires-dist = [
     { name = "model2vec", specifier = ">=0.6.0" },
+    { name = "numpy", specifier = ">=2.3.2" },
     { name = "sentence-transformers", specifier = ">=5.1.0" },
+    { name = "zstandard", specifier = ">=0.24.0" },
 ]
 
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