metadata

datasets:
  - msislam/marc-code-mixed-small
language:
  - de
  - en
  - es
  - fr
metrics:
  - seqeval
widget:
  - text: Hala Madrid y nada más. It means Go Madrid and nothing more.
  - text: Hallo, Guten Tag! how are you?
  - text: >-
      Sie sind gut. How about you? Comment va ta mère? And what about your
      school? Estoy aprendiendo español. Thanks.

Code-Mixed Language Detection using XLM-RoBERTa

Description

This model detects Languages with its boundary by classifying each token. Currently, it supports German (DE), English (EN), Spanish (ES), and French (FR) languages. The model is fine-tuned on xlm-roberta-base.

Training Dataset

The training dataset is based on The Multilingual Amazon Reviews Corpus. The preprocessed dataset can be found here.

Results

'DE': {'precision': 0.9870741390453328,
       'recall': 0.9883516686696866,
       'f1': 0.9877124907612713}
'EN': {'precision': 0.9901617633147289,
       'recall': 0.9914748508098892,
       'f1': 0.9908178720181748}
'ES': {'precision': 0.9912407007439404,
       'recall': 0.9912407007439404,
       'f1': 0.9912407007439406}
'FR': {'precision': 0.9872469872469872,
       'recall': 0.9871314927468414,
       'f1': 0.9871892366188945}

'overall_precision': 0.9888723454274744
'overall_recall': 0.9895702634880803
'overall_f1': 0.9892211813585232
'overall_accuracy': 0.9993651810717168

Usage

The model can be used as follows:

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("msislam/code-mixed-language-detection-XLMRoberta")

model = AutoModelForTokenClassification.from_pretrained("msislam/code-mixed-language-detection-XLMRoberta")

text = 'Hala Madrid y nada más. It means Go Madrid and nothing more.'

tokens = tokenizer(text, add_special_tokens= False, return_tensors="pt")

with torch.no_grad():
  logits = model(**inputs).logits

labels_predicted = logits.argmax(-1)

lang_tag_predicted = [model_best.config.id2label[t.item()] for t in labels_predicted[0]]
lang_tag_predicted