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library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- language-detection
- language-identification
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: polyglot-tagger
results: []
datasets:
- wikimedia/wikipedia
- HuggingFaceFW/finetranslations
- google/smol
- DerivedFunction/nlp-noise-snippets
- DerivedFunction/wikipedia-language-snippets-filtered
- DerivedFunction/finetranslations-filtered
pipeline_tag: token-classification
language:
- en
- es
- fr
- de
- it
- pt
- nl
- vi
- tr
- la
- id
- ms
- af
- sq
- is
- no
- sv
- da
- fi
- hu
- pl
- cs
- ro
- ru
- bg
- uk
- sr
- be
- kk
- mk
- mn
- zh
- ja
- ko
- hi
- ur
- bn
- ta
- te
- mr
- gu
- kn
- ml
- pa
- as
- or
- ar
- fa
- ps
- sd
- ug
- el
- he
- hy
- ka
- am
- km
- lo
- my
- th
---
# Polyglot Tagger: 60L (Experimental)
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base).
It achieves the following results on the evaluation set:
- Loss: 0.0404
- Precision: 0.8848
- Recall: 0.9012
- F1: 0.8929
- Accuracy: 0.9909
## Model description
Introducing Polyglot Tagger 60L, a new way to classify multi-lingual documents. By training specifically on token classification on individual sentences, the model
generalizes well on a variety of languages, while also behaves as a multi-label classifier, and extracts sentences based on its language.
## Intended uses & limitations
This model can be treated as a base model for further fine-tuning on specific language identification extraction tasks.
Note that as a general language tagging model, it can potentially get confused from shared language families or from short texts. For example, English and German, Spanish and Portuguese, and Russian and Ukrainian.
The model is trained on a sentence with a minimum of four tokens, so it may not accurately classify very short and ambigous statements. Note that this model is experimental
and may produce unexpected results compared to generic text classifiers. It is trained on cleaned text, therefore, "messy" text may unexpectedly produce different results.
### Training and Evaluation Data
The model was trained on a synthetic dataset of roughly **2.5 million samples**, covering 60 languages across diverse script families
(Latin, Cyrillic, Indic, Arabic, Han, etc.), from `wikimedia/wikipedia` (up to 200,000 individual sentences, 120,000 reserve from up to 100,000 unique articles,
by taking the first half of Wikipedia after filtering for stubs), `google/smol` (up to 1000 individual sentences),
and `HuggingFaceFW/finetranslations` (up to 50,000 sentences, 30,000 reserve from up to 50,000 unique rows),
in which it is split into a reserve set for pure documents, and a main set for everything else.
A synthetic training row consists of 1-4 individual and mostly independent sentences extracted from various sources.
The data composition follows a strategic curriculum:
* **60% Pure Documents:** Single-language sequences to establish strong baseline profiles for each language.
* **30% Homogenous Mixed:** Documents containing one main language, and clear transitions between two or more languages to train boundary detection.
* **10% Mixed with Noise:** Integration of "neutral" spans including code snippets, mathematical notation, emojis, symbols, and `rot_13` text tagged as `O` or their respective source to reduce hallucination.
### Supported Languages and Limitations (60)
The model supports the following ISO-coded languages:
`af, am, ar, as, be, bg, bn, cs, da, de, el, en, es, fa, fi, fr, gu, he, hi, hu, hy, id, is, it, ja, ka, kk, km, kn, ko, la, lo, ml, mk, mn, mr, ms, my, nl, no, or, pa, pl, ps, pt, ro, ru, sd, sq, sr, sv, ta, te, th, tr, ug, uk, ur, vi, zh`
> Note that Romanized versions of any language is not included in the training set, such as Romanized Russian, and Hindi.
The coverage is as follows from a sample:
Per-group coverage (examples / tokens):
| language | examples | tokens |
| --- | -- | -- |
| English | 47 examples | 3947 tokens |
| Russian | 47 examples | 3665 tokens |
| German | 58 examples | 4625 tokens |
| Japanese | 50 examples | 4188 tokens |
| Chinese | 60 examples | 4131 tokens |
| French | 40 examples | 3723 tokens |
| Spanish | 44 examples | 4756 tokens |
| Portuguese | 27 examples | 2130 tokens |
| Italian | 57 examples | 5178 tokens |
| Polish | 25 examples | 1753 tokens |
| Dutch | 35 examples | 2315 tokens |
| SoutheastAsianLatin | 114 examples | 8861 tokens |
| CentralEuropeanLatin | 125 examples | 9761 tokens |
| Korean | 38 examples | 3958 tokens |
| EastSlavicCyrillic | 85 examples | 7471 tokens |
| Arabic | 45 examples | 2508 tokens |
| BalkanCyrillic | 71 examples | 6231 tokens |
| Hindi | 33 examples | 3251 tokens |
| IndicOther | 261 examples | 40630 tokens |
| CentralAsianCyrillic | 57 examples | 3789 tokens |
| AfricanLatin | 82 examples | 5910 tokens |
| OtherScripts | 269 examples | 28603 tokens |
Top token languages:
ml 8197
it 5178
ta 4903
he 4873
es 4756
de 4625
kn 4613
pa 4457
ja 4188
zh 4131
uk 4007
ko 3958
## Evaluation
> Please note that these results are not indicative that token classification can substitute for sequence classification.
### The model scored the following on `papulca/language-identification`'s test set
|Language | Correct | Total | Accuracy |
|-------------|----------|-------------|--------|
|ar | 114 | 114 | 100.0% |
|bg | 109 | 110 | 99.1% |
|de | 104 | 106 | 98.1% |
|el | 106 | 106 | 100.0% |
|**en*** | **73** | **95** | **76.8%** |
|es | 102 | 104 | 98.1% |
|fr | 102 | 102 | 100.0% |
|hi | 85 | 87 | 97.7% |
|it | 98 | 101 | 97.0% |
|ja | 94 | 94 | 100.0% |
|nl | 95 | 97 | 97.9% |
|pl | 100 | 104 | 96.2% |
|pt | 100 | 101 | 99.0% |
|ru | 116 | 117 | 99.1% |
|th | 108 | 108 | 100.0% |
|tr | 83 | 83 | 100.0% |
|ur | 92 | 94 | 97.9% |
|vi | 87 | 87 | 100.0% |
|zh | 100 | 100 | 100.0% |
> As the training data is slightly biased toward English text, it may produce tokens for English rather than the target language in the Latin family.
### The model scored the following on `mikaberidze/lid200`'s test set, which is derived from `Davlan/sib200`
|Language | Correct | Total | Accuracy
------------|----------|-----------|-----------
|af | 204 | 204 | 100.0%
|am | 204 | 204 | 100.0%
|as | 204 | 204 | 100.0%
|be | 204 | 204 | 100.0%
|bg | 204 | 204 | 100.0%
|bn | 204 | 204 | 100.0%
|cs | 204 | 204 | 100.0%
|da | 203 | 204 |99.5%
|de | 204 | 204 | 100.0%
|el | 204 | 204 | 100.0%
|en | 204 | 204 | 100.0%
|es | 204 | 204 | 100.0%
|fi | 204 | 204 | 100.0%
|fr | 204 | 204 | 100.0%
|gu | 204 | 204 | 100.0%
|he | 204 | 204 | 100.0%
|hi | 204 | 204 | 100.0%
|hu | 204 | 204 | 100.0%
|hy | 204 | 204 | 100.0%
|id | 198 | 204 |97.1%
|is | 204 | 204 | 100.0%
|it | 204 | 204 | 100.0%
|ja | 204 | 204 | 100.0%
|ka | 204 | 204 | 100.0%
|kk | 204 | 204 | 100.0%
|km | 204 | 204 | 100.0%
|kn | 204 | 204 | 100.0%
|ko | 204 | 204 | 100.0%
|lo | 204 | 204 | 100.0%
|mk | 203 | 204 | 99.5%
|ml | 204 | 204 | 100.0%
|mr | 204 | 204 | 100.0%
|my | 204 | 204 | 100.0%
|nl | 203 | 204 |99.5%
|pa | 204 | 204 | 100.0%
|pl | 204 | 204 | 100.0%
|pt | 204 | 204 | 100.0%
|ro | 204 | 204 | 100.0%
|ru | 204 | 204 | 100.0%
|sd | 204 | 204 | 100.0%
|sr | 204 | 204 | 100.0%
|sv | 204 | 204 | 100.0%
|ta | 204 | 204 | 100.0%
|te | 204 | 204 | 100.0%
|th | 204 | 204 | 100.0%
|tr | 204 | 204 | 100.0%
|ug | 204 | 204 | 100.0%
|uk | 204 | 204 | 100.0%
|ur | 204 | 204 | 100.0%
|vi | 204 | 204 | 100.0%
|zh |408 | 408 | 100.0%
> Caution: training data include text from Wikipedia and Finetranslations, which may skew the results.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 72
- eval_batch_size: 36
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 144
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0465 | 0.1447 | 2500 | 0.0819 | 0.7945 | 0.8602 | 0.8260 | 0.9828 |
| 0.0440 | 0.2894 | 5000 | 0.0703 | 0.8023 | 0.8662 | 0.8330 | 0.9843 |
| 0.0351 | 0.4342 | 7500 | 0.0611 | 0.8427 | 0.8800 | 0.8609 | 0.9860 |
| 0.0314 | 0.5789 | 10000 | 0.0593 | 0.8542 | 0.8851 | 0.8694 | 0.9872 |
| 0.0329 | 0.7236 | 12500 | 0.0563 | 0.8394 | 0.8781 | 0.8583 | 0.9868 |
| 0.0281 | 0.8683 | 15000 | 0.0488 | 0.8595 | 0.8853 | 0.8722 | 0.9886 |
| 0.0274 | 1.0130 | 17500 | 0.0477 | 0.8623 | 0.8904 | 0.8761 | 0.9894 |
| 0.0236 | 1.1577 | 20000 | 0.0483 | 0.8675 | 0.8933 | 0.8802 | 0.9894 |
| 0.0235 | 1.3025 | 22500 | 0.0461 | 0.8720 | 0.8933 | 0.8825 | 0.9901 |
| 0.0195 | 1.4472 | 25000 | 0.0439 | 0.8755 | 0.8954 | 0.8853 | 0.9903 |
| 0.0222 | 1.5919 | 27500 | 0.0442 | 0.8765 | 0.8964 | 0.8863 | 0.9901 |
| 0.0194 | 1.7366 | 30000 | 0.0438 | 0.8803 | 0.8993 | 0.8897 | 0.9902 |
| 0.0200 | 1.8814 | 32500 | 0.0404 | 0.8848 | 0.9012 | 0.8929 | 0.9909 |
### Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2 |