roberta-large-kazqad

RoBERTa-Large-KazQAD for Question Answering

Model Description

RoBERTa-Large-KazQAD is a fine-tuned version of RoBERTa-Kaz-Large, specifically optimized for the Question Answering (QA) task using the Kazakh Open-Domain Question Answering Dataset (KazQAD). This model is trained to extract precise answers from given contexts in the Kazakh language.

Fine-Tuning Details

This model was fine-tuned on the KazQAD dataset, which is a Kazakh open-domain question-answering dataset. The fine-tuning process involved adjusting the model's weights to enhance its performance in answering questions based on a given text context. The dataset contains questions and passages from a variety of topics relevant to Kazakh culture, history, geography, and more, making this model highly specialized for understanding and answering questions in the Kazakh language.

Intended Use

This model is designed for open-domain question-answering tasks in the Kazakh language. It can be used to answer factual questions based on the provided context. It is particularly useful for:

• Kazakh Natural Language Processing (NLP) tasks: Enhancing applications involving text comprehension, search engines, chatbots, and virtual assistants in the Kazakh language.
• Research and Educational Purposes: Serving as a benchmark or baseline for further research in Kazakh NLP.

How to Use

You can easily use this model with the Hugging Face Transformers library:

from transformers import AutoModelForQuestionAnswering, AutoTokenizer
import torch

# Load the fine-tuned model and tokenizer
repo_id = 'nur-dev/roberta-large-kazqad'
model = AutoModelForQuestionAnswering.from_pretrained(repo_id)
tokenizer = AutoTokenizer.from_pretrained(repo_id)

# Define the context and question
context = """
Алматы Қазақстанның ең ірі мегаполисі. Алматы – асқақ Тянь-Шань тауы жотасының көкжасыл бауырайынан, 
Іле Алатауының бөктерінде, Қазақстан Республикасының оңтүстік-шығысында, Еуразия құрлығының орталығында орналасқан қала. 
Бұл қаланы «қала-бақ» деп те атайды.
"""
question = "Алматы қаласы Қазақстанның қай бөлігінде орналасқан?"

# Tokenize the input
inputs = tokenizer.encode_plus(
    question, 
    context, 
    add_special_tokens=True, 
    return_tensors="pt"
)

input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]

# Perform inference
with torch.no_grad():
    outputs = model(input_ids=input_ids, attention_mask=attention_mask)
    start_logits = outputs.start_logits
    end_logits = outputs.end_logits

# Find the answer's start and end position
start_index = torch.argmax(start_logits)
end_index = torch.argmax(end_logits)

# Decode the answer from the context
answer = tokenizer.decode(input_ids[0][start_index:end_index + 1])

print(f"Question: {question}")
print(f"Answer: {answer}")

Limitations and Biases

•	Language Specificity: This model is specifically fine-tuned for the Kazakh language and may not perform well in other languages.
•	Context Length: The model has limitations with very long contexts, as it is fine-tuned for input lengths up to 512 tokens.
•	Biases: Like other large pre-trained language models, nur-dev/roberta-large-kazqad may exhibit biases present in its training data. Users should be cautious and critically evaluate the model’s outputs, especially for sensitive applications.

Model Authors

Name: Kadyrbek Nurgali

• Email: [email protected]
• LinkedIn: Kadyrbek Nurgali