bangla-llm-evaluation-datasets
Collection
5 items • Updated • 1
Dataset Viewer
The dataset viewer is not available for this dataset.
The JWT signature verification failed. Check the signing key and the algorithm.
Error code: JWTInvalidSignature
Exception: InvalidSignatureError
Message: Signature verification failed
Traceback: Traceback (most recent call last):
File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
decoded = jwt.decode(
jwt=token,
...<2 lines>...
options=options,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
decoded = self.decode_complete(
jwt,
...<8 lines>...
leeway=leeway,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
decoded = self._jws.decode_complete(
jwt,
...<3 lines>...
detached_payload=detached_payload,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
self._verify_signature(
~~~~~~~~~~~~~~~~~~~~~~^
signing_input,
^^^^^^^^^^^^^^
...<4 lines>...
options=merged_options,
^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
raise InvalidSignatureError("Signature verification failed")
jwt.exceptions.InvalidSignatureError: Signature verification failedNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Data Summary
This is the Bangla-translated version of the OpenBookQA dataset. The dataset was translated using a new method called Expressive Semantic Translation (EST), which combines Google Machine Translation with LLM-based rewriting modifications. This method enhances the semantic accuracy and expressiveness of the translated content. OpenBookQA focuses on advanced question-answering, requiring multi-step reasoning, additional common and commonsense knowledge, and rich text comprehension, similar to open-book exams.
Data Details
Data Instances
Defaults
An example of a 'train' looks as follows:
{
"question_stem": "রাতে যখন একটি গাড়ি আপনার কাছে আসছে",
"choices": {
"text": ["হেডলাইট আরো তীব্র হয়", "হেডলাইট অন্ধকারে ফিরে যায়", "হেডলাইট একটি ধ্রুবক থাকে", "হেডলাইট বন্ধ"],
"label": ["A", "B", "C", "D"]
},
"answerKey": "A"
}
Data Fields
default
The data fields are the same among all splits.
id: astringfeature.question_stem: astringfeature.choices: a dictionary feature containing:text: astringfeature.label: astringfeature.
answerKey: astringfeature.
Data Split
| Split | Number |
|---|---|
| train | 4947 |
| validation | 500 |
| test | 497 |
Citation
TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking
Github Repository: https://github.com/hishab-nlp/lm-evaluation-harness
@misc{nahin2025titullmsfamilybanglallms,
title={TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking},
author={Shahriar Kabir Nahin and Rabindra Nath Nandi and Sagor Sarker and Quazi Sarwar Muhtaseem and Md Kowsher and Apu Chandraw Shill and Md Ibrahim and Mehadi Hasan Menon and Tareq Al Muntasir and Firoj Alam},
year={2025},
eprint={2502.11187},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.11187},
}
- Downloads last month
- 191