aksern/langmonster-1.0

langmonster-1.0

Sentence-level language identification for 19 languages, trained on Tatoeba with a compact transformer encoder.

Model details

Supported languages

eng, spa, fra, deu, rus, bul, ell, ara, cmn, jpn, ita, por, tur, nld, hin, pol, tha, urd, vie

Training data

Tatoeba sentence export (sentences.csv, 13.5M sentences), filtered to the 19 languages above and capped per language.

valid sentences: 2,870,490 (19 languages)
  eng: 250,000     ita: 250,000
  spa: 250,000     por: 250,000
  fra: 250,000     tur: 250,000
  deu: 250,000     nld: 200,710
  rus: 250,000     pol: 137,076
  jpn: 248,866     cmn: 88,787
  ara: 68,485      ell: 42,264
  vie: 32,430      bul: 25,698
  hin: 16,475      tha: 6,848
                    urd: 2,851

Training procedure

• —Optimizer: Adam, lr 1e-3
• —Global batch size 512, 3 epochs (18,575 steps/epoch on the repeated dataset)
• —Hardware: NVIDIA A100 40GB
• —Final: train accuracy 0.9692, loss 0.0915

Performance expectations: strong on the 5 well-represented languages (eng/spa/fra/deu/rus, 250k each). Expect worse results on low-resource languages (urd, tha, hin, bul) and on domains far from Tatoeba text. Reported accuracy is on the training split; there is no held-out validation set for this release.

Usage

python infer.py --model artifacts/model.keras \
  "text on english" "русский текст" "今日の天気は？"

Expected outputs: text on english -> eng (1.000), русский текст -> rus (0.967).

Programmatic (Keras):

import json
import numpy as np
import tensorflow as tf

lang_map = json.load(open("artifacts/languages.json"))
index_to_lang = {v: k for k, v in lang_map.items()}

vocab = json.load(open("artifacts/vocab.json"))
vectorizer = tf.keras.layers.TextVectorization(
    max_tokens=len(vocab), output_mode="int",
    output_sequence_length=128, standardize="lower_and_strip_punctuation",
)
vectorizer.set_vocabulary(vocab)

model = tf.keras.models.load_model("artifacts/model.keras")
tokens = tf.cast(vectorizer(tf.constant(["hello world"])), tf.int32)
logits = model.predict(tokens)
print(index_to_lang[int(np.argmax(logits[0]))])

Limitations

• —Trained only on Tatoeba sentences; informal/social text and other domains may degrade accuracy.
• —Tatoeba has no Swahili sentences, so swa is excluded despite being in the original target list.
• —Very low-resource classes (urd, tha, hin, bul) have only thousands of samples and are unreliable.
• —Sentence-level only; longer documents should be split into sentences before classification.

License

MIT. Data is from Tatoeba, whose sentences are subject to their own licensing terms.