51 Languages Classifier
51 Languages Classifier is a highly efficient language identification model that can accurately classify text into 51 languages. With a remarkable precision of 98.89%, it's capable of distinguishing between languages such as Afrikaans, Amharic, Arabic, and many more. Trained on a massive dataset of over 1 million utterances, this model is designed to handle a wide range of languages and dialects. Its impressive performance is evident in its evaluation results, which show high scores in precision, recall, and f1-score. Whether you're working with multilingual text or need to identify languages for your project, 51 Languages Classifier is a reliable and efficient tool to get the job done.
Deploy Model in Dataloop Pipelines
51 Languages Classifier fits right into a Dataloop Console pipeline, making it easy to process and manage data at scale. It runs smoothly as part of a larger workflow, handling tasks like annotation, filtering, and deployment without extra hassle. Whether it's a single step or a full pipeline, it connects with other nodes easily, keeping everything running without slowdowns or manual work.
Table of Contents
Model Overview
The 51-languages-classifier model is a powerful tool for language identification. It can recognize 51 different languages, from Afrikaans to Welsh, and is trained on a massive dataset called MASSIVE, which contains over 1 million utterances across these languages.
Capabilities
What can it do?
The model uses a technique called sequence classification to identify the language of a given text. It can distinguish between languages with non-Latin scripts, handle text in various formats, including single sentences or short paragraphs, and identify languages such as Afrikaans, Amharic, Arabic, Azerbaijani, Bengali, Chinese, Danish, English, Spanish, French, Hebrew, Hindi, Italian, Japanese, Korean, Malay, Russian, Swedish, Thai, Turkish, Vietnamese, and many more.
How accurate is it?
The model has been evaluated on its performance, and the results show that it’s very accurate, with an overall accuracy of 98.89%. This means that it can correctly identify the language of a given text in almost all cases.
What languages can it identify?
The model supports an impressive 51 languages, including some of the most widely spoken languages in the world.
Performance
Speed
The model can process and classify text in a matter of milliseconds. This is because it uses a powerful algorithm that can quickly analyze the text and determine the language.
Accuracy
The model has an impressive accuracy rate of 98.89%. This means that out of 100 texts, the model can correctly identify the language 98.89 times.
Efficiency
The model is very efficient when it comes to using system resources. It can run on a variety of devices, from small laptops to large servers.
Example Use Case
Let’s say you have a sentence in an unknown language: “פרק הבא בפודקאסט בבקשה”. You can use the 51-languages-classifier model to identify the language. The model would output: [{'label': 'he-IL', 'score': 0.9998375177383423}], which means it’s very confident that the language is Hebrew (he-IL).
Evaluation Results
Here are the evaluation results for the model:
| Language | Precision | Recall | F1-Score | Support |
|---|---|---|---|---|
| af-ZA | 0.9821 | 0.9805 | 0.9813 | 2974 |
| am-ET | 1.0000 | 1.0000 | 1.0000 | 2974 |
| … | … | … | … | … |
As you can see, the model performs very well across all languages, with high precision, recall, and F1-scores.
Limitations
While the 51-languages-classifier model is a powerful tool for language identification, it’s not perfect. It may struggle to understand the context of a given text, especially if the text is short or contains ambiguous language. Additionally, the model may not perform well on low-resource languages or languages with limited training data.
Format
The model uses a transformer-based architecture, specifically a variant of the XLM-Roberta model. This model is designed to handle text inputs in multiple languages.
Supported Data Formats
The model accepts text input in the form of strings. You can use any text data, but it’s essential to preprocess the text by tokenizing it using the AutoTokenizer from the transformers library.
Input Requirements
To use this model, you need to:
- Install the
transformerslibrary usingpip install transformers. - Import the necessary classes:
AutoTokenizer,AutoModelForSequenceClassification, andTextClassificationPipeline. - Load the pre-trained model and tokenizer using the
qanastek/51-languages-classifiermodel name. - Create a
TextClassificationPipelineinstance with the loaded model and tokenizer. - Pass your text input to the
classifierinstance to get the predicted language label.
Example Code
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
model_name = 'qanastek/51-languages-classifier'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
text_input = "פרק הבא בפודקאסט בבקשה"
res = classifier(text_input)
print(res)
Output Format
The model outputs a list of dictionaries, where each dictionary contains the predicted language label and its corresponding score.
[{'label': 'he-IL', 'score': 0.9998375177383423}]
In this example, the model predicts that the input text is in Hebrew (he-IL) with a confidence score of approximately 99.99%.
