XLM-RoBERTa Large trained on Dravidian Language QA

Overview

Language model: XLM-RoBERTa-lg Language: Multilingual, focussed on Tamil & Hindi Downstream-task: Extractive QA Eval data: K-Fold on Training Data

Hyperparameters

batch_size = 4
base_LM_model = "xlm-roberta-large"
learning_rate = 1e-5

optimizer = AdamW
weight_decay = 1e-2
epsilon = 1e-8
max_grad_norm = 1.0

lr_schedule = LinearWarmup
warmup_proportion = 0.2

max_seq_len = 256
doc_stride=128
max_query_length=64

Performance

Evaluated on our human annotated dataset with 1000 tamil question-context pairs [link]

  "em": 77.536,
  "f1": 85.665

Usage

In Transformers

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "Srini99/FYP_TamilQA"

model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
    'question': 'யாரால் பொங்கல் சிறப்பாகக் கொண்டாடப்படுகிறது?',
    'context': 'பொங்கல் என்பது தமிழர்களால் சிறப்பாகக் கொண்டாடப்படும் ஓர் அறுவடைப் பண்டிகை ஆகும்.'
}
res = nlp(QA_input)

Srini99
/

TQA