README.md

metadata

license: apache-2.0
task_categories:
  - text-classification
language:
  - multilingual
tags:
  - language-identification
  - unigram
  - tokenizer
  - tinyaya
pretty_name: TinyAya LID Experiment Logs

TinyAya LID — Models, Eval Data & Training Artifacts

Artifacts for the Contrastive UniLID project: language identification using LLM tokenizer vocabularies (TinyAya 261k BPE→Unigram), trained on GlotLID-C, evaluated on CommonLID.

Source code: github.com/divyanshsinghvi/tinyAyaLid

Note: GlotLID-C training corpus is not included here — it can be re-downloaded from cis-lmu/glotlid-corpus. This repo only ships the eval data, models, training weights, and LLM cache.

---

Structure

.
├── models/                          # Trained .unilid model files + eval JSONs
│   ├── tinyaya_v3_200k/             # Best TinyAya model — 200k samples/lang
│   ├── tinyaya_v3_100k/             # TinyAya, 100k samples/lang
│   ├── tinyaya_soft_full/           # TinyAya, full GlotLID-C corpus
│   ├── mistral_v3_200k/             # Mistral-Nemo 131k tokenizer comparison
│   ├── scratch_v3_200k/             # Scratch 100k vocab comparison
│   ├── commonlid_20pct/             # Trained on 20% CommonLID split (TinyAya)
│   ├── commonlid_50pct/             # Trained on 50% CommonLID split (TinyAya)
│   ├── commonlid_20pct_mistral/     # 20% CommonLID split (Mistral)
│   ├── commonlid_50pct_mistral/     # 50% CommonLID split (Mistral)
│   ├── commonlid_20pct_scratch/     # 20% CommonLID split (Scratch)
│   └── commonlid_50pct_scratch/     # 50% CommonLID split (Scratch)
│
├── data/
│   ├── commonlid/                   # CommonLID evaluation corpus (fastText format)
│   │   ├── commonlid_full.txt       # Full test set (373k samples, 109 tags)
│   │   ├── commonlid_train.txt      # Train split
│   │   ├── commonlid_test.txt       # Test split
│   │   ├── commonlid_50pct_test.txt # 50% split
│   │   ├── commonlid_80pct_test.txt # 80% split
│   │   ├── commonlid_50perlang.txt  # 50 samples/lang subsample
│   │   ├── commonlid_150perlang.txt # 150 samples/lang subsample
│   │   ├── commonlid_200perlang.txt # 200 samples/lang subsample
│   │   ├── commonlid_20pct_by_lang/ # Per-language files (20pct split)
│   │   └── commonlid_50pct_by_lang/ # Per-language files (50pct split)
│   │
│   └── misc/                        # Small training experiment files
│       ├── train_quick.txt
│       ├── train_quick_test.txt
│       ├── train_1k.txt
│       ├── train_1k_test.txt
│       └── train_test.txt
│
├── training_weights/                # Per-language unigram log-prob dists from soft EM (compressed)
│   └── *.tar.gz                     # One tarball per experiment config
│
└── cache/                           # Cached LLM API responses (two-stage eval)
    └── cache.tar.gz

Data Formats

• fastText format (__label__<lang_Script> <text>): all CommonLID files
• Plain text (one sentence per line): misc training files

Languages

• CommonLID eval: 109 language tags (373,230 samples in commonlid_full.txt)
• Alias mapping (CommonLID→model individual code): ara→arb, aze→azj, bik→bcl, est→ekk, lav→lvs, mlg→plt, msa→zsm, orm→gaz, swa→swh, tgl→fil, uzb→uzn, zho→cmn

Reproducing Training

To retrain a model, download GlotLID-C separately:

from datasets import load_dataset
ds = load_dataset("cis-lmu/glotlid-corpus")

Then run train.py from the source repo using the desired tokenizer.

Contributors

Divyansh Singhvi, Megha Agarwal. Mentored by Julia Kreutzer.