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---
license: cc-by-nc-4.0
language:
- ar
- az
- bg
- de
- el
- en
- es
- fr
- hi
- it
- ja
- nl
- pl
- pt
- ru
- sw
- th
- tr
- ur
- vi
- zh
pipeline_tag: text-classification
tags:
- language detect
---
# Multilingual Language Detection Model
## Model Description
This repository contains a multilingual language detection model based on the XLM-RoBERTa base architecture. The model is capable of distinguishing between 21 different languages including Arabic, Azerbaijani, Bulgarian, German, Greek, English, Spanish, French, Hindi, Italian, Japanese, Dutch, Polish, Portuguese, Russian, Swahili, Thai, Turkish, Urdu, Vietnamese, and Chinese.
## How to Use
You can use this model directly with a pipeline for text classification, or you can use it with the `transformers` library for more custom usage, as shown in the example below.
### Quick Start
First, install the transformers library if you haven't already:
```bash
pip install transformers
```
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("LocalDoc/language_detection")
model = AutoModelForSequenceClassification.from_pretrained("LocalDoc/language_detection")
# Prepare text
text = "Əlqasım oğulları vorzakondu"
encoded_input = tokenizer(text, return_tensors='pt', truncation=True, max_length=512)
# Prediction
model.eval()
with torch.no_grad():
outputs = model(**encoded_input)
# Process the outputs
logits = outputs.logits
probabilities = torch.nn.functional.softmax(logits, dim=-1)
predicted_class_index = probabilities.argmax().item()
labels = ["az", "ar", "bg", "de", "el", "en", "es", "fr", "hi", "it", "ja", "nl", "pl", "pt", "ru", "sw", "th", "tr", "ur", "vi", "zh"]
predicted_label = labels[predicted_class_index]
print(f"Predicted Language: {predicted_label}")
```
Training Performance
The model was trained over three epochs, showing consistent improvement in accuracy and loss:
<b>Epoch 1:</b> Training Loss: 0.0127, Validation Loss: 0.0174, Accuracy: 0.9966, F1 Score: 0.9966
<b>Epoch 2:</b> Training Loss: 0.0149, Validation Loss: 0.0141, Accuracy: 0.9973, F1 Score: 0.9973
<b>Epoch 3:</b> Training Loss: 0.0001, Validation Loss: 0.0109, Accuracy: 0.9984, F1 Score: 0.9984
Test Results
The model achieved the following results on the test set:
Loss: 0.0133
Accuracy: 0.9975
F1 Score: 0.9975
Precision: 0.9975
Recall: 0.9975
Evaluation Time: 17.5 seconds
Samples per Second: 599.685
Steps per Second: 9.424
Licensing
This model is released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You are free to use, modify, and distribute this model non-commercially, provided you attribute the original creation.
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