HellaSwag-Hindi#
Overview#
HellaSwag-Hindi is a Hindi translation of the HellaSwag commonsense sentence-completion benchmark’s
full validation set. The context stem stays in English; the 4 candidate continuations are translated
into Hindi, so the model must connect an English scenario to its most plausible Hindi-phrased ending.
Sourced from ai4bharat/hellaswag-translated (the canonical name; the older ai4bharat/hellaswag-hi
ID redirects here), the same dataset used by lighteval’s community_hellaswag_hin tasks.
Task Description#
Task Type: Commonsense Sentence Completion (mixed-language)
Input: An English context sentence with 4 Hindi-language candidate continuations
Output: Correct answer letter
Coverage: Full HellaSwag validation set (10,042 examples)
Key Features#
Full HellaSwag validation set: 10,042 examples with gold labels
English context stem paired with Hindi-translated candidate endings (mixed-language setup)
Same dataset used by lighteval’s
community_hellaswag_hintask suite
Evaluation Notes#
Default configuration uses 0-shot evaluation (validation split, the only labeled split available — HellaSwag’s
testsplit ships without gold labels)Loads from ModelScope by default (mirrored as
ai4bharat/hellaswag-translated), no token required
Properties#
Property |
Value |
|---|---|
Benchmark Name |
|
Dataset ID |
|
Paper |
N/A |
Tags |
|
Metrics |
|
Default Shots |
0-shot |
Evaluation Split |
|
Data Statistics#
Metric |
Value |
|---|---|
Total Samples |
10,042 |
Prompt Length (Mean) |
1021.77 chars |
Prompt Length (Min/Max) |
367 / 1977 chars |
Sample Example#
Subset: hi
{
"input": [
{
"id": "f96132ec",
"content": "Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D.\n\nA man is sitting on a roof. he\n\nA) वह स्की की एक जोड़ी को लपेटने के लिए रैप का उपयोग कर रहा है।\nB) यह स्तर की टाइलों को चीर रहा है।\nC) वह एक रूबिक क्यूब पकड़े हुए है।\nD) एक छत पर छत खींचना शुरू करता है।"
}
],
"choices": [
"वह स्की की एक जोड़ी को लपेटने के लिए रैप का उपयोग कर रहा है।",
"यह स्तर की टाइलों को चीर रहा है।",
"वह एक रूबिक क्यूब पकड़े हुए है।",
"एक छत पर छत खींचना शुरू करता है।"
],
"target": "D",
"id": 0,
"group_id": 0,
"metadata": {
"activity_label": "Roof shingle removal"
}
}
Prompt Template#
Prompt Template:
Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}.
{question}
{choices}
Usage#
Using CLI#
evalscope eval \
--model YOUR_MODEL \
--api-url OPENAI_API_COMPAT_URL \
--api-key EMPTY_TOKEN \
--datasets hellaswag_hi \
--limit 10 # Remove this line for formal evaluation
Using Python#
from evalscope import run_task
from evalscope.config import TaskConfig
task_cfg = TaskConfig(
model='YOUR_MODEL',
api_url='OPENAI_API_COMPAT_URL',
api_key='EMPTY_TOKEN',
datasets=['hellaswag_hi'],
limit=10, # Remove this line for formal evaluation
)
run_task(task_cfg=task_cfg)