| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 7 decomposed |
| Times Matched | 0 | 0 |
Performs binary sentiment classification (positive/negative) on Korean text using a small ELECTRA discriminator model fine-tuned on the NSMC (Naver Sentiment Movie Comments) dataset. The model leverages ELECTRA's replaced-token detection pretraining approach combined with task-specific fine-tuning on 200K Korean movie reviews, enabling efficient sentiment inference with 23.5M parameters. Inference runs locally via PyTorch/Hugging Face Transformers without requiring API calls, supporting batch processing and custom confidence thresholds.
Unique: Uses ELECTRA's discriminator-based pretraining (replaced-token detection) rather than MLM, enabling smaller model size (23.5M params vs 110M for BERT-base) while maintaining competitive accuracy on Korean sentiment tasks. Fine-tuned specifically on NSMC's 200K movie reviews with domain-specific Korean tokenization, making it optimized for review-like Korean text patterns.
vs alternatives: Smaller and faster than KoBERT-base (110M params) or multilingual BERT variants while maintaining NSMC-specific accuracy; more specialized for Korean sentiment than generic mBERT but less generalizable to non-review domains than larger models.
Processes multiple Korean text samples in parallel batches using Hugging Face Transformers' DataCollator with dynamic padding, which pads sequences to the longest sample in each batch rather than a fixed max length. This reduces computational waste and memory overhead when processing variable-length Korean text. Supports configurable batch sizes and automatic device placement (CPU/GPU), enabling efficient throughput for production inference pipelines without manual padding logic.
Unique: Leverages Hugging Face Transformers' native DataCollator with dynamic padding, which automatically computes optimal padding per batch rather than padding to fixed max_length. This is implemented via the collate_fn in DataLoader, reducing wasted computation on padding tokens by ~30-50% for variable-length Korean text.
vs alternatives: More memory-efficient than padding all sequences to fixed 512 tokens; simpler than manual bucketing strategies but less flexible than custom ONNX-optimized inference engines for ultra-low-latency requirements.
Loads model weights from Hugging Face Hub using safetensors format (a secure, fast serialization standard) instead of pickle, with automatic version management and caching. The model is stored as a public repository with git-based versioning, allowing reproducible downloads of specific commits/tags. Safetensors format enables faster deserialization (~10x vs pickle) and eliminates arbitrary code execution risks during weight loading, making it suitable for production and untrusted environments.
Unique: Uses safetensors format for model serialization, which is a secure, fast alternative to pickle that prevents arbitrary code execution during deserialization. Combined with Hugging Face Hub's git-based versioning, this enables reproducible, version-pinned model loading with built-in security guarantees.
vs alternatives: Safer than pickle-based model loading (eliminates code execution risk); faster deserialization than PyTorch's native format; more reproducible than downloading from custom URLs due to Hub's version control integration.
Tokenizes Korean text using ELECTRA's pretrained WordPiece tokenizer, which was trained on Korean corpora and includes morphological awareness for Korean-specific linguistic patterns (e.g., particles, verb conjugations, compound words). The tokenizer handles Korean-specific edge cases like spacing conventions, Hangul decomposition, and subword segmentation optimized for Korean morphology. Supports both encoding (text → token IDs) and decoding (token IDs → text) with configurable special tokens and truncation strategies.
Unique: Uses a Korean-specific WordPiece tokenizer trained on Korean corpora, which includes morphological awareness for Korean linguistic patterns (particles, verb conjugations, compound words). This is more effective than generic multilingual tokenizers for Korean text, reducing subword fragmentation and improving model performance.
vs alternatives: More morphologically aware than generic multilingual tokenizers (mBERT) but less interpretable than dedicated Korean morphological analyzers (Mecab, Okt); optimized for ELECTRA's pretraining but not customizable for domain-specific vocabulary.
Provides a pretrained ELECTRA discriminator checkpoint that can be fine-tuned for downstream Korean text classification tasks beyond sentiment analysis. The model's learned representations capture Korean linguistic patterns from pretraining, enabling efficient transfer learning with minimal labeled data. Supports standard fine-tuning workflows (adding task-specific head, freezing/unfreezing layers, learning rate scheduling) via Hugging Face Transformers' Trainer API or custom PyTorch training loops.
Unique: Provides a Korean-specific ELECTRA discriminator pretrained on large Korean corpora, enabling efficient transfer learning for downstream Korean tasks. Unlike generic multilingual models, it captures Korean-specific linguistic patterns (morphology, syntax, semantics) learned during pretraining, reducing fine-tuning data requirements.
vs alternatives: More efficient for Korean tasks than fine-tuning from multilingual BERT or starting from scratch; smaller than KoBERT-base (23.5M vs 110M params) enabling faster fine-tuning and inference; less general-purpose than larger models but more specialized for Korean NLP.
Outputs softmax-normalized probability distributions over sentiment classes (positive/negative), enabling confidence-based filtering and decision-making. The model produces logits that are converted to probabilities via softmax, allowing downstream systems to reject low-confidence predictions or apply different handling strategies based on confidence thresholds. Supports both hard predictions (argmax class) and soft predictions (probability distributions) for flexible integration into decision pipelines.
Unique: Provides raw logits and softmax probabilities for both sentiment classes, enabling confidence-based filtering and decision-making without additional uncertainty quantification. The small model size (23.5M params) makes confidence scores computationally cheap to generate at scale.
vs alternatives: Simpler than Bayesian approaches (Monte Carlo Dropout, ensemble methods) but less robust to distribution shift; sufficient for basic confidence filtering but requires post-hoc calibration for well-calibrated probabilities.
Enables evaluation of AI systems' ability to perform chained mathematical operations (addition, subtraction, multiplication, division, comparisons) across both structured tables and unstructured text extracted from SEC filings. The dataset provides ground-truth question-answer pairs where answers require synthesizing data from multiple locations within earnings reports and applying sequential arithmetic operations, testing whether models can decompose complex financial queries into discrete computational steps.
Unique: Combines real SEC filing documents (not synthetic) with crowdsourced questions requiring multi-step arithmetic, creating a hybrid dataset that tests both domain knowledge extraction and quantitative reasoning in a single evaluation task. Unlike generic math word problems, answers require locating figures within 10+ page documents first.
vs alternatives: More challenging than DROP or SVAMP because it requires financial domain knowledge AND document retrieval before arithmetic, whereas generic math benchmarks assume figures are already extracted
Assesses whether AI systems understand financial terminology, accounting concepts, and domain-specific metrics by requiring them to answer questions about real earnings reports from S&P 500 companies. The dataset tests recognition of financial line items (revenue, COGS, operating expenses, net income), ability to distinguish between different financial statements (income statement vs balance sheet), and understanding of financial ratios and metrics without explicit instruction on their definitions.
Unique: Uses authentic SEC filings rather than synthetic financial data, exposing models to real-world accounting variations, footnote complexity, and the actual structure of professional financial documents. This tests transfer learning from general text to specialized domain without domain-specific pretraining.
vs alternatives: More authentic than synthetic financial QA datasets because it uses real earnings reports with their inherent complexity, but narrower than general financial knowledge benchmarks because it focuses only on historical data interpretation
FinQA scores higher at 60/100 vs koelectra-small-v3-nsmc at 47/100. koelectra-small-v3-nsmc leads on adoption and ecosystem, while FinQA is stronger on quality.
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Enables evaluation of AI systems' ability to extract numerical data from both structured HTML/text tables and unstructured prose within the same document, then reason over the extracted values. The dataset contains questions where relevant data appears in different formats — some figures are in formatted tables with clear row/column headers, while others are embedded in narrative text or footnotes — requiring robust parsing and entity linking before computation can occur.
Unique: Combines structured table data with unstructured narrative in the same evaluation, forcing systems to handle format heterogeneity and resolve references across different data representations. Most table QA datasets use clean, isolated tables; this tests real-world document complexity.
vs alternatives: More realistic than isolated table QA benchmarks (like SQA or WikiTableQuestions) because it requires handling narrative context and format mixing, but simpler than full document understanding because tables are already in text format (no OCR needed)
Provides a curated, crowdsourced-annotated dataset of 8,281 question-answer pairs with multi-step reasoning requirements, enabling systematic evaluation of AI systems on financial numerical reasoning. The dataset includes quality control mechanisms through crowdworker annotation, answer validation against ground truth, and coverage across diverse financial metrics and company types within the S&P 500, creating a reproducible evaluation standard for the financial AI community.
Unique: Provides a publicly available, reproducible benchmark specifically designed for financial numerical reasoning with real SEC filings, enabling standardized comparison across different financial AI systems. Most financial datasets are proprietary or synthetic; this is open-source and authentic.
vs alternatives: More specialized and challenging than generic QA benchmarks (SQuAD, MRQA) because it requires financial domain knowledge and multi-step arithmetic, but narrower in scope than comprehensive financial understanding benchmarks because it focuses only on numerical reasoning
Assesses AI systems' ability to perform multi-hop reasoning by requiring them to locate and combine information from different sections of earnings reports. Questions may require finding a figure in the income statement, then locating a related metric in the balance sheet, then performing arithmetic across both — testing whether models can maintain context across document boundaries and understand relationships between different financial statement sections.
Unique: Embeds multi-hop reasoning requirements within authentic financial documents where hops correspond to real relationships between financial statement sections, rather than synthetic reasoning chains. This tests whether models understand domain structure, not just generic multi-hop patterns.
vs alternatives: More realistic than synthetic multi-hop datasets (HotpotQA, 2WikiMultiHopQA) because reasoning hops follow actual financial relationships, but less controlled because document structure varies and reasoning paths are implicit rather than explicitly annotated
Enables evaluation of whether AI systems can identify which arithmetic operations (addition, subtraction, multiplication, division, comparison) are required to answer financial questions, then execute them correctly. The dataset implicitly tests operation selection — a question asking 'what is the profit margin' requires division (net income / revenue), while 'what is total assets' requires addition — forcing models to understand financial semantics before applying math.
Unique: Embeds arithmetic operation selection within financial domain context, requiring models to understand that 'margin' semantically maps to division and 'total' maps to addition. This tests semantic grounding of operations, not just arithmetic execution.
vs alternatives: More semantically grounded than generic math word problem datasets because operation selection is implicit in financial terminology, but less explicit than datasets with annotated operation types because operations must be inferred
Provides evaluation capability for AI systems to compare financial metrics across multiple S&P 500 companies or aggregate metrics across different time periods within the same company's earnings reports. While individual questions reference single documents, the dataset structure enables evaluation of systems that can retrieve and compare relevant companies, requiring understanding of which metrics are comparable across entities and how to normalize for company size or accounting differences.
Unique: Provides a foundation for evaluating cross-company financial comparison by including diverse S&P 500 companies with different business models and scales, enabling assessment of whether systems can normalize and compare metrics appropriately. Most financial QA datasets focus on single-document questions.
vs alternatives: Enables cross-company evaluation unlike single-document QA datasets, but requires external retrieval and comparison logic because the dataset itself contains only single-document questions