Model Card for mDeBERTa-v3-base-myXNLI

mDeBERTa-v3-base-myXNLI is a transformer model for text classification English and Myanmar (Burmese).

It is based on multilingual DeBERTa v3 model and fine-tuned using myXNLI dataset on the Natural Language Inference task in English and Myanmar.

Thus it is useful for Natural Language Inference and related tasks such as Zero-shot Text Classification on both English and Myanmar data.

Model Details

Model type: Transformer Encoder
Language(s) (NLP): Fine-tuned for Myanmar (Burmese) and English
License: MIT
Finetuned from model: mDeBERTa v3 base https://huggingface.co/microsoft/mdeberta-v3-base
Paper : Myanmar XNLI https://www.researchsquare.com/article/rs-4329843
Demo : A demo of Zero-shot Text Classification in Myanmar can be found on this page.

Bias, Risks, and Limitations

Please refer to the papers for original foundation model: DeBERTa https://arxiv.org/abs/2006.03654 and DeBERTaV3 https://arxiv.org/abs/2111.09543.

How to Get Started with the Model

Use the code below to get started with the model for zero-shot classification task.

from transformers import pipeline

classifier = pipeline(task="zero-shot-classification", model="akhtet/mDeBERTa-v3-base-myXNLI", framework="pt")

output = classifier("မြန်မာ့စီးပွားရေးမှာ ရွှေ နဲ့ ဒေါ်လာက အရေးပါသလို ဒေါ်လာစျေးပေါ်မူတည်ပြီး အခြားစားသောက်ကုန်ပစ္စည်းတွေကလည်း လိုက်ပါပြောင်းလဲလေ့ ရှိပါတယ်။",
    candidate_labels=["commerce", "fashion", "music", "politics", "sports"],
)

print (output)
# output
# {'sequence': 'မြန်မာ့စီးပွားရေးမှာ ရွှေ နဲ့ ဒေါ်လာက အရေးပါသလို ဒေါ်လာစျေးပေါ်မူတည်ပြီး အခြားစားသောက်ကုန်ပစ္စည်းတွေကလည်း လိုက်ပါပြောင်းလဲလေ့ ရှိပါတယ်။',
# 'labels': ['commerce', 'politics', 'fashion', 'music', 'sports'],
# 'scores': [0.8995707631111145, 0.048580411821603775, 0.035297513008117676, 0.009092549793422222, 0.007458842825144529]}

Fore more details on zero-shot classification, please refer to HuggingFace documentation https://huggingface.co/tasks/zero-shot-classification

Training Details

The model is fine-tuned on myXNLI dataset https://huggingface.co/datasets/akhtet/myXNLI. The English portion of myXNLI is from XNLI dataset.

From this dataset, 4 different copies training data from myXNLI were concatenated, each with sentence pairs in en-en, en-my, my-en and my-my combinations.

Training on cross-matched language data as above improved the NLI accuracy over training separately in each language. This approach was inspired by another model https://huggingface.co/joeddav/xlm-roberta-large-xnli

The model was fine-tuned using this combined dataset for a single epoch.

Evaluation

This model has been evaluted on myXNLI testset for Myanmar accuracy. We also provide the accuracy for English using XNLI testset.

Model	English accuracy	Myanmar accuracy
mDeBERTa-v3-base-myXNLI	88.02	80.99

Citation

[More Information Needed]

Model Card Contact

Aung Kyaw Htet

akhtet
/

mDeBERTa-v3-base-myanmar-xnli