FinQA ranks higher at 60/100 vs deberta-v3-xsmall-zeroshot-v1.1-all-33 at 38/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | deberta-v3-xsmall-zeroshot-v1.1-all-33 | FinQA |
|---|---|---|
| Type | Model | Dataset |
| UnfragileRank | 38/100 | 60/100 |
| Adoption | 0 | 1 |
| Quality |
| 0 |
| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 7 decomposed |
| Times Matched | 0 | 0 |
Classifies text into arbitrary user-defined categories without requiring labeled training data, using DeBERTa-v3's contrastive learning architecture to map input text and candidate labels into a shared embedding space, then computing similarity scores to determine the most probable class. The model was fine-tuned on 33 diverse NLI datasets to generalize across domain-specific classification tasks, enabling dynamic category definition at inference time without retraining.
Unique: Trained on 33 diverse NLI datasets (vs typical 1-3 dataset fine-tuning) to maximize generalization across unseen classification domains; uses DeBERTa-v3's disentangled attention mechanism which separates content and position embeddings, improving semantic understanding for zero-shot transfer compared to BERT-based alternatives
vs alternatives: Smaller and faster than zero-shot alternatives (BART, T5) while maintaining competitive accuracy through NLI pre-training; outperforms GPT-3.5 zero-shot on structured classification tasks with 100x lower latency and no API costs
Provides pre-quantized weights and ONNX Runtime-compatible serialization to enable sub-100ms inference on CPU and edge devices. The xsmall variant (22M parameters) is quantized to int8 precision, reducing model size from ~90MB to ~45MB while maintaining classification accuracy within 1-2% of full precision. ONNX export enables hardware-accelerated inference across CPU, GPU, and specialized accelerators (TPU, NPU) without PyTorch dependency.
Unique: Pre-quantized int8 weights provided alongside full-precision checkpoint, eliminating need for users to perform quantization; ONNX export includes optimized graph transformations for DeBERTa's disentangled attention, preserving architectural benefits during inference
vs alternatives: Faster CPU inference than PyTorch baseline (3-5x speedup via ONNX Runtime) and smaller model size than unquantized alternatives, enabling deployment to resource-constrained environments where larger zero-shot models (BART, T5) are infeasible
Scores each candidate label independently against input text, enabling multi-label classification where a single text can be assigned multiple categories simultaneously. Unlike single-label classification, the model computes similarity scores for each label without forcing a winner-take-all decision, allowing downstream applications to set custom thresholds per label or use all scores for ranking-based decisions.
Unique: Leverages NLI training to score labels independently without explicit multi-label fine-tuning; DeBERTa's attention mechanism allows the model to evaluate each label's relevance to the input text in isolation, avoiding label interference that occurs in models trained with multi-label loss functions
vs alternatives: More flexible than single-label classifiers and avoids the computational overhead of true multi-label models (which require exponential label combinations); enables threshold-based filtering that single-label models cannot provide
While trained exclusively on English NLI data, the model can perform zero-shot classification on non-English text through cross-lingual transfer, leveraging multilingual token embeddings in the DeBERTa-v3 tokenizer. When given non-English input text and English candidate labels, the model maps both to a shared semantic space, enabling classification in languages not explicitly seen during training. Performance degrades gracefully with language distance from English.
Unique: Achieves cross-lingual transfer without explicit multilingual training through DeBERTa-v3's shared token embeddings; NLI training on English data generalizes to non-English input because the entailment task (does premise entail hypothesis?) is language-agnostic at the semantic level
vs alternatives: Simpler and faster than maintaining separate language-specific models; outperforms naive machine translation + English classification on latency-sensitive systems, though accuracy is lower than true multilingual models (mBERT, XLM-R)
Processes multiple text samples in a single batch while allowing each sample to have a different set of candidate labels, without requiring padding or masking of label sets. The model computes classification scores for each (text, label) pair independently, enabling efficient vectorized inference where batch size and label set heterogeneity do not impact computational complexity. Useful for scenarios where label sets vary by sample (e.g., product categorization where different products have different valid categories).
Unique: Supports heterogeneous label sets per sample without padding or masking, leveraging DeBERTa's efficient attention mechanism to compute independent (text, label) scores in parallel; enables true dynamic classification where label vocabulary is not fixed at model initialization
vs alternatives: More flexible than fixed-vocabulary classifiers; avoids padding overhead of models that require uniform label set sizes, reducing memory usage and latency for variable-label-set scenarios
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 deberta-v3-xsmall-zeroshot-v1.1-all-33 at 38/100. deberta-v3-xsmall-zeroshot-v1.1-all-33 leads on ecosystem, while FinQA is stronger on adoption and quality.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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