What can hellaswag do?

commonsense-reasoning-benchmark-dataset-loading, multi-format-dataset-export-and-serialization, train-validation-test-split-management, streaming-dataset-iteration-for-memory-constrained-environments, schema-aware-data-validation-and-type-inference, cross-framework-dataset-compatibility-and-adapter-generation, dataset-filtering-and-subset-selection-by-metadata, dataset-versioning-and-reproducible-snapshot-management

hellaswag

DatasetFree

Dataset by Rowan. 3,02,975 downloads.

Open Source

/ 100

8 capabilities

Capabilities8 decomposed

commonsense-reasoning-benchmark-dataset-loading

Medium confidence

Loads a curated dataset of 302,975 multiple-choice video-grounded commonsense reasoning examples from HuggingFace's datasets library, with built-in support for streaming, caching, and format conversion (parquet, arrow, CSV). The dataset is structured as context-question-answer tuples derived from ActivityNet Captions video descriptions, enabling models to predict plausible next events in video scenarios. Integrates directly with HuggingFace's `datasets` library for lazy loading, train/validation/test splits, and automatic schema validation.

Solves for

Load and benchmark language models on commonsense reasoning tasks without manual data curationTrain or fine-tune models on video-grounded event prediction with standardized evaluation splitsEvaluate model performance on out-of-distribution commonsense reasoning compared to human baselinesIntegrate commonsense reasoning evaluation into model training pipelines and CI/CD workflows

Best for

ML researchers evaluating language model reasoning capabilities

Teams building commonsense reasoning benchmarks for model evaluation

Developers training instruction-tuned or RLHF models requiring diverse reasoning tasks

Requires

Python 3.7+

HuggingFace datasets library (pip install datasets)

pandas or polars for data manipulation (optional but recommended)

Limitations

Dataset is English-only; no multilingual variants for cross-lingual evaluation

Video descriptions are text-only; original video frames not included, limiting multimodal reasoning evaluation

Fixed train/validation/test splits cannot be customized; no built-in stratification by difficulty or category

What makes it unique

Combines video-grounded context from ActivityNet Captions with adversarially-collected wrong answers (via crowdsourcing) to create harder commonsense reasoning tasks than typical multiple-choice datasets; uses HuggingFace's streaming infrastructure for efficient loading of 300K+ examples without requiring full downloads

vs alternatives

Larger and more adversarially-challenging than SWAG (88K examples) with better video grounding than pure text-based commonsense datasets like CommonsenseQA, while maintaining standardized HuggingFace integration for reproducible benchmarking

multi-format-dataset-export-and-serialization

Medium confidence

Exports the hellaswag dataset to multiple serialization formats (parquet, arrow, CSV, JSON) via HuggingFace's datasets library, with automatic schema inference, compression options, and batch processing support. Handles columnar storage (parquet/arrow) for efficient analytics and row-oriented formats (CSV/JSON) for downstream consumption. Supports streaming export for datasets larger than available RAM, with configurable batch sizes and partitioning strategies.

Solves for

Export dataset to parquet for efficient storage and columnar analytics in data warehousesConvert dataset to CSV/JSON for integration with non-Python ML frameworks or web applicationsPartition large datasets across multiple files for distributed processing on Spark or DaskCreate dataset snapshots in different formats for reproducibility and version control

Best for

Data engineers building ETL pipelines that consume multiple format types

Teams migrating between ML frameworks (PyTorch, TensorFlow, JAX) with different data loaders

Researchers sharing datasets in format-agnostic ways across institutions

Requires

Python 3.7+

HuggingFace datasets library

pyarrow (for parquet/arrow export)

Limitations

Parquet export requires pyarrow library; CSV export loses nested column structure if present

No built-in compression for JSON export; files can be 3-5x larger than parquet equivalents

Streaming export requires manual batch size tuning; no automatic optimization for network bandwidth

What makes it unique

Leverages HuggingFace's unified dataset abstraction to support format conversion without custom serialization code; uses Apache Arrow as intermediate representation, enabling zero-copy transfers between formats and native support for streaming large datasets

vs alternatives

More flexible than pandas-only export (supports Arrow/parquet natively) and simpler than manual Spark/Dask pipelines, with automatic schema preservation across format conversions

train-validation-test-split-management

Medium confidence

Provides pre-defined train/validation/test splits for the hellaswag dataset via HuggingFace's split parameter, with deterministic sampling and no data leakage between splits. Splits are computed once during dataset creation and cached locally, enabling reproducible train/eval workflows. The dataset uses stratified sampling to ensure balanced distribution of difficulty levels and answer patterns across splits.

Solves for

Load train/validation/test splits without manual data partitioning or risk of data leakageReproduce published benchmark results by using identical splits across experimentsEvaluate models on held-out test set with confidence that no training data was seenCompare model performance across multiple runs with consistent evaluation methodology

Best for

Researchers publishing benchmark results requiring reproducible evaluation

Teams training models with strict train/test separation requirements

Practitioners comparing against published baselines on standardized splits

Requires

Python 3.7+

HuggingFace datasets library

Sufficient RAM to load at least one split (~500MB for train split)

Limitations

Splits are fixed and cannot be customized (e.g., 80/10/10 vs 70/15/15 ratios)

No cross-validation support; only single train/val/test split available

Stratification strategy is not documented; cannot verify balance across difficulty levels

What makes it unique

Uses HuggingFace's deterministic split mechanism with cached metadata, ensuring identical splits across different machines and Python versions without requiring manual seed management or data shuffling

vs alternatives

More reproducible than sklearn's train_test_split (no random seed management needed) and simpler than manual stratified sampling, with built-in caching to avoid recomputation

streaming-dataset-iteration-for-memory-constrained-environments

Medium confidence

Enables streaming iteration over the hellaswag dataset without loading the entire 302K examples into memory, using HuggingFace's streaming API to fetch batches on-demand from the Hub. Each batch is fetched, processed, and discarded, keeping memory footprint constant regardless of dataset size. Supports configurable batch sizes, prefetching, and parallel workers for efficient I/O.

Solves for

Train models on large datasets on machines with limited RAM (e.g., edge devices, shared clusters)Process dataset examples one-at-a-time for online learning or continual evaluationReduce initial download time by streaming only required splits instead of full datasetImplement memory-efficient data augmentation or preprocessing pipelines

Best for

Researchers training on resource-constrained hardware (mobile, edge, shared clusters)

Teams implementing online learning or continual evaluation workflows

Practitioners prototyping models without committing to full dataset downloads

Requires

Python 3.7+

HuggingFace datasets library with streaming support

Stable internet connection with sufficient bandwidth (>1 Mbps recommended)

Limitations

Streaming adds network latency (~50-200ms per batch) compared to local disk access

No random access; must iterate sequentially, making stratified sampling difficult

Requires stable internet connection; network interruptions require restart from last checkpoint

What makes it unique

Implements streaming via HuggingFace's Hub infrastructure with automatic caching of fetched batches, enabling efficient iteration without requiring local storage while maintaining deterministic ordering for reproducibility

vs alternatives

More memory-efficient than loading full dataset (constant RAM vs linear in dataset size) and simpler than implementing custom streaming loaders, with built-in fault tolerance and resumable iteration

schema-aware-data-validation-and-type-inference

Medium confidence

Automatically infers and validates the schema of hellaswag examples (context string, question string, multiple-choice endings list, label integer) using HuggingFace's schema inference engine. Validates that each example conforms to expected types and structure, catching malformed or missing fields before model training. Schema is cached and reused across loads, enabling fast validation without re-scanning the dataset.

Solves for

Catch data quality issues (missing fields, type mismatches) early in the pipeline before trainingUnderstand dataset structure programmatically for dynamic model input layer configurationGenerate type hints for downstream processing code (e.g., PyTorch DataLoader)Validate custom dataset modifications without manual schema specification

Best for

ML engineers building robust data pipelines with automatic quality checks

Teams integrating hellaswag with type-checked frameworks (Pydantic, dataclasses)

Researchers extending the dataset with custom fields requiring schema validation

Requires

Python 3.7+

HuggingFace datasets library

pyarrow for schema representation

Limitations

Schema inference is automatic but may infer incorrect types for edge cases (e.g., numeric strings as integers)

No custom validation rules; only type checking, not semantic validation (e.g., label must be in [0, 3])

Schema is read-only; cannot modify inferred types without reloading dataset

What makes it unique

Uses Apache Arrow's schema inference to automatically detect column types and structure without manual specification, with caching to avoid re-inference on subsequent loads

vs alternatives

More automatic than pandas dtype inference (handles complex types like lists) and simpler than Pydantic validation, with tight integration to HuggingFace's data loading pipeline

cross-framework-dataset-compatibility-and-adapter-generation

Medium confidence

Provides adapters to convert hellaswag into framework-specific formats (PyTorch DataLoader, TensorFlow Dataset, JAX numpy arrays) via HuggingFace's ecosystem integrations. Each adapter handles batching, padding, tokenization, and type conversion automatically. Supports lazy evaluation (streaming) and eager loading (in-memory) modes depending on framework requirements.

Solves for

Load hellaswag directly into PyTorch training loops without custom data loading codeUse hellaswag with TensorFlow/Keras models via tf.data.Dataset APIConvert hellaswag to numpy arrays for JAX or scikit-learn workflowsSwitch between frameworks without re-implementing data loading logic

Best for

ML practitioners using multiple frameworks (PyTorch, TensorFlow, JAX) in same project

Teams standardizing on HuggingFace ecosystem for framework-agnostic data handling

Researchers comparing models across frameworks with identical data pipelines

Requires

Python 3.7+

HuggingFace datasets library

Target framework: torch, tensorflow, jax, or numpy

Limitations

Adapters require framework-specific libraries (torch, tensorflow, jax) to be installed

Tokenization and padding are not automatic; require separate preprocessing step

No built-in batching for variable-length sequences; requires custom collate functions

What makes it unique

Leverages HuggingFace's unified dataset abstraction to generate framework-specific adapters without duplicating data or requiring manual conversion code, with support for both eager and lazy evaluation modes

vs alternatives

More flexible than framework-specific dataset classes (supports multiple frameworks) and simpler than manual data loading code, with automatic batching and type conversion

dataset-filtering-and-subset-selection-by-metadata

Medium confidence

Filters hellaswag examples by metadata attributes (e.g., activity category, difficulty level, answer distribution) using HuggingFace's filter API with predicate functions. Supports efficient filtering via columnar operations (parquet/arrow) without loading full dataset into memory. Filtered subsets are cached for reuse across experiments.

Solves for

Create domain-specific subsets (e.g., only cooking or sports activities) for targeted evaluationFilter by difficulty level to evaluate model performance on easy vs hard examplesRemove examples with specific characteristics (e.g., ambiguous answers) for cleaner evaluationCreate balanced subsets with equal representation of answer patterns for fairness evaluation

Best for

Researchers analyzing model performance on domain-specific or difficulty-stratified subsets

Teams evaluating fairness and bias across different example categories

Practitioners debugging model failures on specific example types

Requires

Python 3.7+

HuggingFace datasets library

Optional: pandas for complex filtering logic

Limitations

Filtering requires loading dataset into memory or using columnar operations (slower than indexed queries)

No built-in metadata for difficulty or category; requires manual annotation or external mapping

Filter predicates are Python functions; cannot be optimized by query planner

What makes it unique

Implements filtering via HuggingFace's columnar operations (Arrow) for efficient predicate pushdown, avoiding full dataset materialization while maintaining lazy evaluation semantics

vs alternatives

More efficient than pandas filtering (columnar operations vs row-wise) and simpler than SQL queries, with native integration to HuggingFace's caching and streaming infrastructure

dataset-versioning-and-reproducible-snapshot-management

Medium confidence

Manages dataset versions and snapshots via HuggingFace's Hub versioning system, enabling reproducible access to specific dataset versions (e.g., 'revision=main' or 'revision=v1.0'). Each version is immutable and cached locally, preventing silent data changes between experiments. Supports rollback to previous versions and tracking of version history via Git-like semantics.

Solves for

Ensure reproducibility by pinning dataset version in experiment configsTrack dataset evolution and compare model performance across versionsRollback to previous dataset version if issues are discovered in current versionShare exact dataset snapshot with collaborators for reproducible research

Best for

Researchers publishing results requiring exact dataset reproducibility

Teams managing long-running experiments with potential dataset updates

Practitioners collaborating across institutions with version-controlled datasets

Requires

Python 3.7+

HuggingFace datasets library

HuggingFace Hub account (for pushing new versions)

Limitations

Versioning is managed by HuggingFace Hub; no local version control without Git integration

Version history is not queryable; requires manual tracking of version changes

No automatic version detection; must explicitly specify revision parameter

What makes it unique

Leverages HuggingFace Hub's Git-based versioning to provide immutable dataset snapshots with automatic caching and rollback support, without requiring separate version control infrastructure

vs alternatives

More convenient than manual dataset versioning (Git, DVC) and simpler than data warehouse versioning, with tight integration to HuggingFace's ecosystem and automatic caching

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

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3 shared capabilities

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multi-format dataset loading and transformationchain-of-thought reasoning dataset sampling and curation

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multi-format-dataset-export-and-conversionbenchmark-task-filtering-and-stratification

2 shared capabilities

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2 shared capabilities

Benchmark31

promptbench

PromptBench is a powerful tool designed to scrutinize and analyze the interaction of large language models with various prompts. It provides a convenient infrastructure to simulate **black-box** adversarial **prompt attacks** on the models and evaluate their performances.

dataset-loader-with-multi-format-support

1 shared capability

Framework43

PromptBench

Microsoft's unified LLM evaluation and prompt robustness benchmark.

dataset loader with multi-format support and automatic preprocessing

1 shared capability

Best For

✓ML researchers evaluating language model reasoning capabilities
✓Teams building commonsense reasoning benchmarks for model evaluation
✓Developers training instruction-tuned or RLHF models requiring diverse reasoning tasks
✓Data engineers building ETL pipelines that consume multiple format types
✓Teams migrating between ML frameworks (PyTorch, TensorFlow, JAX) with different data loaders
✓Researchers sharing datasets in format-agnostic ways across institutions
✓Researchers publishing benchmark results requiring reproducible evaluation
✓Teams training models with strict train/test separation requirements

Known Limitations

⚠Dataset is English-only; no multilingual variants for cross-lingual evaluation
⚠Video descriptions are text-only; original video frames not included, limiting multimodal reasoning evaluation
⚠Fixed train/validation/test splits cannot be customized; no built-in stratification by difficulty or category
⚠Parquet format requires pandas/polars for efficient filtering; no native SQL query support
⚠No temporal metadata for video sequences; treats each example as independent without temporal context
⚠Parquet export requires pyarrow library; CSV export loses nested column structure if present

Requirements

Python 3.7+HuggingFace datasets library (pip install datasets)pandas or polars for data manipulation (optional but recommended)~2GB disk space for full dataset download and cachingInternet connection for initial dataset download from HuggingFace HubHuggingFace datasets librarypyarrow (for parquet/arrow export)pandas (for CSV export)

Input / Output

Accepts: dataset identifier string (e.g., 'Rowan/hellaswag'), split parameter (train/validation/test), optional filtering/sampling parameters, HuggingFace Dataset object, format string (parquet/arrow/csv/json), optional compression codec (gzip, snappy, brotli), split parameter: 'train', 'validation', or 'test', streaming=True parameter, batch_size parameter (default 1000), num_workers parameter for parallel fetching, target framework string (pytorch/tensorflow/jax/numpy), filter predicate function (takes example dict, returns bool), optional batch_size for efficient filtering, revision parameter: branch name, tag, or commit hash, optional: version metadata (author, changelog)

Produces: Apache Arrow tables (in-memory columnar format), pandas DataFrames, polars DataFrames, streaming iterators for memory-efficient processing, parquet files for disk storage, parquet files (columnar, compressed), arrow files (columnar, uncompressed), CSV files (row-oriented, text), JSON files (row-oriented, text), HuggingFace Dataset object containing examples for specified split, metadata: split size, number of examples, schema, Iterator yielding batches of examples, Each batch: dict with keys 'ctx', 'question', 'endings', 'label', pyarrow.Schema object with column names and types, validation errors (if any) with field names and expected vs actual types, PyTorch DataLoader with batched tensors, TensorFlow Dataset with batched tensors, JAX pytree with numpy arrays, numpy arrays for direct consumption, Filtered HuggingFace Dataset object, metadata: original size, filtered size, filter selectivity, HuggingFace Dataset object for specified version, version metadata: revision, timestamp, author

UnfragileRank

Adoption15%(35% weight)

Quality17%(25% weight)

Ecosystem60%(20% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Dataset

8 capabilities

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About

hellaswag — a dataset on HuggingFace with 3,02,975 downloads

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Data Sources

huggingface

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Capabilities8 decomposed

commonsense-reasoning-benchmark-dataset-loading

Medium confidence

Solves for

Best for

ML researchers evaluating language model reasoning capabilities

Teams building commonsense reasoning benchmarks for model evaluation

Developers training instruction-tuned or RLHF models requiring diverse reasoning tasks

Requires

Python 3.7+

HuggingFace datasets library (pip install datasets)

pandas or polars for data manipulation (optional but recommended)

Limitations

Dataset is English-only; no multilingual variants for cross-lingual evaluation

Video descriptions are text-only; original video frames not included, limiting multimodal reasoning evaluation

Fixed train/validation/test splits cannot be customized; no built-in stratification by difficulty or category

What makes it unique

vs alternatives

multi-format-dataset-export-and-serialization

Medium confidence

Solves for

Best for

Data engineers building ETL pipelines that consume multiple format types

Teams migrating between ML frameworks (PyTorch, TensorFlow, JAX) with different data loaders

Researchers sharing datasets in format-agnostic ways across institutions

Requires

Python 3.7+

HuggingFace datasets library

pyarrow (for parquet/arrow export)

Limitations

Parquet export requires pyarrow library; CSV export loses nested column structure if present

No built-in compression for JSON export; files can be 3-5x larger than parquet equivalents

Streaming export requires manual batch size tuning; no automatic optimization for network bandwidth

What makes it unique

vs alternatives

More flexible than pandas-only export (supports Arrow/parquet natively) and simpler than manual Spark/Dask pipelines, with automatic schema preservation across format conversions

train-validation-test-split-management

Medium confidence

Solves for

Best for

Researchers publishing benchmark results requiring reproducible evaluation

Teams training models with strict train/test separation requirements

Practitioners comparing against published baselines on standardized splits

Requires

Python 3.7+

HuggingFace datasets library

Sufficient RAM to load at least one split (~500MB for train split)

Limitations

Splits are fixed and cannot be customized (e.g., 80/10/10 vs 70/15/15 ratios)

No cross-validation support; only single train/val/test split available

Stratification strategy is not documented; cannot verify balance across difficulty levels

What makes it unique

vs alternatives

More reproducible than sklearn's train_test_split (no random seed management needed) and simpler than manual stratified sampling, with built-in caching to avoid recomputation

streaming-dataset-iteration-for-memory-constrained-environments

Medium confidence

Solves for

Best for

Researchers training on resource-constrained hardware (mobile, edge, shared clusters)

Teams implementing online learning or continual evaluation workflows

Practitioners prototyping models without committing to full dataset downloads

Requires

Python 3.7+

HuggingFace datasets library with streaming support

Stable internet connection with sufficient bandwidth (>1 Mbps recommended)

Limitations

Streaming adds network latency (~50-200ms per batch) compared to local disk access

No random access; must iterate sequentially, making stratified sampling difficult

Requires stable internet connection; network interruptions require restart from last checkpoint

What makes it unique

vs alternatives

More memory-efficient than loading full dataset (constant RAM vs linear in dataset size) and simpler than implementing custom streaming loaders, with built-in fault tolerance and resumable iteration

schema-aware-data-validation-and-type-inference

Medium confidence

Solves for

Best for

ML engineers building robust data pipelines with automatic quality checks

Teams integrating hellaswag with type-checked frameworks (Pydantic, dataclasses)

Researchers extending the dataset with custom fields requiring schema validation

Requires

Python 3.7+

HuggingFace datasets library

pyarrow for schema representation

Limitations

Schema inference is automatic but may infer incorrect types for edge cases (e.g., numeric strings as integers)

No custom validation rules; only type checking, not semantic validation (e.g., label must be in [0, 3])

Schema is read-only; cannot modify inferred types without reloading dataset

What makes it unique

Uses Apache Arrow's schema inference to automatically detect column types and structure without manual specification, with caching to avoid re-inference on subsequent loads

vs alternatives

More automatic than pandas dtype inference (handles complex types like lists) and simpler than Pydantic validation, with tight integration to HuggingFace's data loading pipeline

cross-framework-dataset-compatibility-and-adapter-generation

Medium confidence

Solves for

Best for

ML practitioners using multiple frameworks (PyTorch, TensorFlow, JAX) in same project

Teams standardizing on HuggingFace ecosystem for framework-agnostic data handling

Researchers comparing models across frameworks with identical data pipelines

Requires

Python 3.7+

HuggingFace datasets library

Target framework: torch, tensorflow, jax, or numpy

Limitations

Adapters require framework-specific libraries (torch, tensorflow, jax) to be installed

Tokenization and padding are not automatic; require separate preprocessing step

No built-in batching for variable-length sequences; requires custom collate functions

What makes it unique

vs alternatives

More flexible than framework-specific dataset classes (supports multiple frameworks) and simpler than manual data loading code, with automatic batching and type conversion

dataset-filtering-and-subset-selection-by-metadata

Medium confidence

Solves for

Best for

Researchers analyzing model performance on domain-specific or difficulty-stratified subsets

Teams evaluating fairness and bias across different example categories

Practitioners debugging model failures on specific example types

Requires

Python 3.7+

HuggingFace datasets library

Optional: pandas for complex filtering logic

Limitations

Filtering requires loading dataset into memory or using columnar operations (slower than indexed queries)

No built-in metadata for difficulty or category; requires manual annotation or external mapping

Filter predicates are Python functions; cannot be optimized by query planner

What makes it unique

Implements filtering via HuggingFace's columnar operations (Arrow) for efficient predicate pushdown, avoiding full dataset materialization while maintaining lazy evaluation semantics

vs alternatives

More efficient than pandas filtering (columnar operations vs row-wise) and simpler than SQL queries, with native integration to HuggingFace's caching and streaming infrastructure

dataset-versioning-and-reproducible-snapshot-management

Medium confidence

Solves for

Best for

Researchers publishing results requiring exact dataset reproducibility

Teams managing long-running experiments with potential dataset updates

Practitioners collaborating across institutions with version-controlled datasets

Requires

Python 3.7+

HuggingFace datasets library

HuggingFace Hub account (for pushing new versions)

Limitations

Versioning is managed by HuggingFace Hub; no local version control without Git integration

Version history is not queryable; requires manual tracking of version changes

No automatic version detection; must explicitly specify revision parameter

What makes it unique

Leverages HuggingFace Hub's Git-based versioning to provide immutable dataset snapshots with automatic caching and rollback support, without requiring separate version control infrastructure

vs alternatives

More convenient than manual dataset versioning (Git, DVC) and simpler than data warehouse versioning, with tight integration to HuggingFace's ecosystem and automatic caching

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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hellaswag

Capabilities8 decomposed

commonsense-reasoning-benchmark-dataset-loading

multi-format-dataset-export-and-serialization

train-validation-test-split-management

streaming-dataset-iteration-for-memory-constrained-environments

schema-aware-data-validation-and-type-inference

cross-framework-dataset-compatibility-and-adapter-generation

dataset-filtering-and-subset-selection-by-metadata

dataset-versioning-and-reproducible-snapshot-management

Related Artifactssharing capabilities

gsm8k

OpenThoughts-1k-sample

SWE-bench_Verified

glue

promptbench

PromptBench

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to hellaswag

Are you the builder of hellaswag?

Get the weekly brief

Data Sources

hellaswag

Capabilities8 decomposed

commonsense-reasoning-benchmark-dataset-loading

multi-format-dataset-export-and-serialization

train-validation-test-split-management

streaming-dataset-iteration-for-memory-constrained-environments

schema-aware-data-validation-and-type-inference

cross-framework-dataset-compatibility-and-adapter-generation

dataset-filtering-and-subset-selection-by-metadata

dataset-versioning-and-reproducible-snapshot-management

Related Artifactssharing capabilities

gsm8k

OpenThoughts-1k-sample

SWE-bench_Verified

glue

promptbench

PromptBench

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to hellaswag

Are you the builder of hellaswag?

Get the weekly brief

Data Sources