Datasets:
File size: 3,990 Bytes
5eb10b3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | ---
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](https://github.com/divyanshsinghvi/tinyAyaLid)
> **Note**: GlotLID-C training corpus is **not included** here — it can be re-downloaded from [`cis-lmu/glotlid-corpus`](https://huggingface.co/datasets/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:
```python
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.
|