metadata

language:
  - multilingual
  - ar
  - bg
  - de
  - el
  - en
  - es
  - fr
  - hi
  - it
  - ja
  - nl
  - pl
  - pt
  - ru
  - sw
  - th
  - tr
  - ur
  - vi
  - zh
license: cc-by-4.0
tags:
  - peft
  - LoRA
  - language-detection
  - xlm-roberta-base
datasets:
  - papluca/language-identification
metrics:
  - accuracy
  - f1
inference: false
base_model: xlm-roberta-base
model-index:
  - name: xlm-roberta-base-lora-language-detection
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: papluca/language-identification
          type: papluca/language-identification
        metrics:
          - type: accuracy
            value: 99.43
            name: Accuracy
          - type: f1
            value: 99.43
            name: F1 Score

xlm-roberta-base-lora-language-detection

This model is a fine-tuned version of xlm-roberta-base on the Language Identification dataset. Using the PEFT-LoRA method to only fine-tune a small number of (extra) model parameters, thereby greatly decreasing the computational and storage costs.

Model description

This model is an XLM-RoBERTa transformer model with a classification head on top (i.e. a linear layer on top of the pooled output). For additional information please refer to the xlm-roberta-base model card or to the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al.

Intended uses & limitations

You can directly use this model as a language detector, i.e. for sequence classification tasks. Currently, it supports the following 20 languages:

arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), japanese (ja), dutch (nl), polish (pl), portuguese (pt), russian (ru), swahili (sw), thai (th), turkish (tr), urdu (ur), vietnamese (vi), and chinese (zh)

Training and evaluation data

The model was fine-tuned on the Language Identification dataset, which consists of text sequences in 20 languages. The training set contains 70k samples, while the validation and test sets 10k each. The average accuracy on the test set is 99.4% (this matches the average macro/weighted F1-score being the test set perfectly balanced). A more detailed evaluation is provided by the following table.

Language	Precision	Recall	F1-score	support
ar	1.000	0.998	0.999	500
bg	0.992	1.000	0.996	500
de	1.000	1.000	1.000	500
el	1.000	1.000	1.000	500
en	0.992	0.992	0.992	500
es	0.994	0.992	0.993	500
fr	0.998	0.998	0.998	500
hi	0.945	1.000	0.972	500
it	1.000	0.984	0.992	500
ja	1.000	1.000	1.000	500
nl	0.996	0.992	0.994	500
pl	0.992	0.988	0.990	500
pt	0.988	0.986	0.987	500
ru	0.998	0.996	0.997	500
sw	0.992	0.994	0.993	500
th	1.000	1.000	1.000	500
tr	1.000	1.000	1.000	500
ur	1.000	0.964	0.982	500
vi	1.000	1.000	1.000	500
zh	1.000	1.000	1.000	500

Benchmarks

As a baseline to compare xlm-roberta-base-lora-language-detection against, we have used the Python langid library. Since it comes pre-trained on 97 languages, we have used its .set_languages() method to constrain the language set to our 20 languages. The average accuracy of langid on the test set is 98.5%. More details are provided by the table below.

Language	Precision	Recall	F1-score	support
ar	0.990	0.970	0.980	500
bg	0.998	0.964	0.981	500
de	0.992	0.944	0.967	500
el	1.000	0.998	0.999	500
en	1.000	1.000	1.000	500
es	1.000	0.968	0.984	500
fr	0.996	1.000	0.998	500
hi	0.949	0.976	0.963	500
it	0.990	0.980	0.985	500
ja	0.927	0.988	0.956	500
nl	0.980	1.000	0.990	500
pl	0.986	0.996	0.991	500
pt	0.950	0.996	0.973	500
ru	0.996	0.974	0.985	500
sw	1.000	1.000	1.000	500
th	1.000	0.996	0.998	500
tr	0.990	0.968	0.979	500
ur	0.998	0.996	0.997	500
vi	0.971	0.990	0.980	500
zh	1.000	1.000	1.000	500

Using the model for inference

# pip install -q loralib transformers
# pip install -q git+https://github.com/huggingface/peft.git@main

import torch
from peft import PeftConfig, PeftModel
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    pipeline,
)

peft_model_id = "dominguesm/xlm-roberta-base-lora-language-detection"

# Load the Peft model config
peft_config = PeftConfig.from_pretrained(peft_model_id)

# Load the base model config
base_config = AutoConfig.from_pretrained(peft_config.base_model_name_or_path)

# Load the base model
base_model = AutoModelForSequenceClassification.from_pretrained(
    peft_config.base_model_name_or_path, config=base_config
)

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(peft_config.base_model_name_or_path)

# Load the inference model
inference_model = PeftModel.from_pretrained(base_model, peft_model_id)

# Load the pipeline
pipe = pipeline("text-classification", model=inference_model, tokenizer=tokenizer)


def detect_lang(text: str) -> str:
    # This code runs on CPU, so we use torch.cpu.amp.autocast to perform
    # automatic mixed precision.
    with torch.cpu.amp.autocast():
        # or `with torch.cuda.amp.autocast():`
        pred = pipe(text)
    return pred


detect_lang(
    "Cada qual sabe amar a seu modo; o modo, pouco importa; o essencial é que saiba amar."
)
# [{'label': 'pt', 'score': 0.9959434866905212}]

Training procedure

Fine-tuning was done via the Trainer API. Here is the Jupyter notebook with the training code.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 64
eval_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
num_epochs: 2

Training results

The validation results on the valid split of the Language Identification dataset are summarised here below.

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
1.4403	1.0	1094	0.0591	0.9952	0.9952
0.0256	2.0	2188	0.0272	0.9955	0.9955

In short, it achieves the following results on the validation set:

Loss: 0.0298
Accuracy: 0.9946
F1: 0.9946

Framework versions

torch 1.13.1+cu116
datasets 2.10.1
sklearn 1.2.1
transformers 4.27.0.dev0
langid 1.1.6
peft 0.3.0.dev0

Note

This study was fully based and inspired by the xlm-roberta-base-language-detection model, developed by Luca Papariello.