Xlm Roberta Base Language Detection

Table of Contents

Model Overview

Meet the XLM-RoBERTa Base Language Detection model! This model is a fine-tuned version of the XLM-RoBERTa model, specifically designed for language detection tasks.

What can it do?

This model can detect the language of a given text sequence. It’s trained on a dataset of 70,000 text samples in 20 different languages, including Arabic, Bulgarian, German, and many more.

Alternatives

All MiniLM L6 V2 2389 Sentence Embeddings

Bert Base Uncased 1831 Uncased BERT model

Xlm Roberta Large 323 Multilingual Language Model

Capabilities

The XLM-RoBERTa Base Language Detection model is a powerful tool for detecting languages in text. It can accurately identify the language of a given text sequence, supporting 20 languages, including Arabic, Bulgarian, German, and many more.

Primary Tasks

• Language detection: The model can identify the language of a text sequence with high accuracy.
• Sequence classification: The model can classify text sequences into different languages.

Strengths

• High accuracy: The model achieves an average accuracy of 99.6% on the test set, outperforming the baseline model, langid, which has an average accuracy of 98.5%.
• Support for multiple languages: The model supports 20 languages, making it a versatile tool for language detection tasks.

Unique Features

• Fine-tuned on Language Identification dataset: The model was fine-tuned on a large dataset of text sequences in 20 languages, making it well-suited for language detection tasks.
• XLM-RoBERTa architecture: The model uses the XLM-RoBERTa architecture, which is a powerful transformer model that has been shown to achieve state-of-the-art results on many natural language processing tasks.

Examples

Detect the language of this text: 'Bonjour, comment allez-vous?' French (fr)

Identify the language of this sentence: '¿Cómo estás?' Spanish (es)

What language is this text written in: 'Hallo, wie geht es dir?' German (de)

Performance

The XLM-RoBERTa Base Language Detection model is a powerhouse when it comes to language detection tasks. But how well does it really perform?

Speed

The model is incredibly fast, thanks to its efficient architecture. It can process large amounts of text data in a matter of seconds. But what does that mean in numbers? Let’s take a look:

Task	Time
Language detection on a single text sample	0.05 seconds
Language detection on a batch of 100 text samples	0.5 seconds

As you can see, the model is blazingly fast, making it perfect for applications where speed is crucial.

Accuracy

But speed is nothing without accuracy. Luckily, XLM-RoBERTa Base Language Detection delivers on that front as well. It achieves an impressive average accuracy of 99.6% on the Language Identification dataset, which consists of 20 languages.

Here’s a breakdown of the model’s performance on each language:

Language	Precision	Recall	F1-score
Arabic (ar)	0.998	0.996	0.997
Bulgarian (bg)	0.998	0.964	0.981
…	…	…	…
Vietnamese (vi)	0.992	1.000	0.996
Chinese (zh)	1.000	1.000	1.000

As you can see, the model performs exceptionally well on all languages, with some languages achieving perfect scores.

Efficiency

But how does the model compare to other language detection models? Let’s take a look at the benchmarks:

Model	Average Accuracy
XLM-RoBERTa Base Language Detection	99.6%
Langid	98.5%

As you can see, XLM-RoBERTa Base Language Detection outperforms the popular Langid library by a significant margin.

Limitations

XLM-RoBERTa Base Language Detection is a powerful language detection tool, but it’s not perfect. Let’s take a closer look at some of its limitations.

Limited Language Support

XLM-RoBERTa Base Language Detection only supports 20 languages, which might not be enough for some use cases. If you need to detect languages beyond this list, you might need to look into other models like langid.

Data Bias

The model was trained on a specific dataset, which might not be representative of all languages or dialects. This could lead to biased results, especially for languages with limited representation in the training data.

Accuracy Variations

While XLM-RoBERTa Base Language Detection achieves high accuracy on the test set (99.6%), there are some variations in accuracy across languages. For example, Arabic and Bulgarian have slightly lower F1-scores compared to other languages.

Comparison to Other Models

XLM-RoBERTa Base Language Detection outperforms langid on the test set, but the difference in accuracy is relatively small (1.1%). This suggests that other models might be suitable alternatives, depending on your specific use case.

Getting Started

You can use this model via the high-level pipeline API or by loading the tokenizer and model separately. Here’s an example:

from transformers import pipeline

text = ["Brevity is the soul of wit.", "Amor, ch'a nullo amato amar perdona."]
model_ckpt = "papluca/xlm-roberta-base-language-detection"
pipe = pipeline("text-classification", model=model_ckpt)
pipe(text, top_k=1, truncation=True)

Alternatively, you can use the tokenizer and model separately:

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

text = ["Brevity is the soul of wit.", "Amor, ch'a nullo amato amar perdona."]
model_ckpt = "papluca/xlm-roberta-base-language-detection"
tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
model = AutoModelForSequenceClassification.from_pretrained(model_ckpt)

inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
    preds = torch.softmax(logits, dim=-1)

# Map raw predictions to languages
id2lang = model.config.id2label
vals, idxs = torch.max(preds, dim=1)
{id2lang[k.item()]: v.item() for k, v in zip(idxs, vals)}

Tasks and rank

Text Classification 46th

Language Modelling 139th

Cross-Lingual Transfer 10th

Learn More About the Ranking System