README.md

---
language:
- uk
license: cc-by-4.0
extra_gated_prompt: This dataset contains 428K parsed Ukrainian court decisions for
  temporal drift research. Access is gated to track usage. Please describe your intended
  use case.
extra_gated_fields:
  Affiliation: text
  Use case: text
  I agree to use this dataset for research purposes only: checkbox
task_categories:
- text-classification
task_ids:
- multi-class-classification
tags:
- legal
- court-decisions
- temporal-drift
- concept-drift
- judgment-prediction
- ukrainian
- EDRSR
- LEXTREME
- temporal-robustness
- benchmark
size_categories:
- 100K<n<1M
configs:
- config_name: all
  data_files:
  - split: train
    path: all/train-*
  - split: validation
    path: all/validation-*
  - split: test
    path: all/test-*
- config_name: full_scale
  data_files:
  - split: train
    path: full_scale/train-*
  - split: validation
    path: full_scale/validation-*
  - split: test
    path: full_scale/test-*
- config_name: hybrid_war
  data_files:
  - split: train
    path: hybrid_war/train-*
  - split: validation
    path: hybrid_war/validation-*
  - split: test
    path: hybrid_war/test-*
- config_name: pre_war
  data_files:
  - split: train
    path: pre_war/train-*
  - split: validation
    path: pre_war/validation-*
  - split: test
    path: pre_war/test-*
dataset_info:
  config_name: all
  features:
  - name: text
    dtype: string
  - name: label
    dtype: string
  - name: epoch
    dtype: string
  - name: adjudication_date
    dtype: string
  - name: jurisdiction
    dtype: string
  - name: doc_id
    dtype: string
  - name: language
    dtype: string
  splits:
  - name: train
    num_bytes: 3916234379
    num_examples: 342460
  - name: validation
    num_bytes: 627921896
    num_examples: 42807
  - name: test
    num_bytes: 664630637
    num_examples: 42808
  download_size: 1989932016
  dataset_size: 5208786912
---

# UA-Temporal-Drift: Temporal Concept Drift Benchmark for Legal Judgment Prediction

A benchmark dataset of **428,075 Ukrainian court decisions** spanning 2008–2026, designed to measure temporal concept drift in legal NLP models. Decisions are organized into three temporal epochs reflecting major geopolitical disruptions to the Ukrainian judicial system, with chronological train/validation/test splits within each epoch.

## Motivation

Legal NLP benchmarks (LexGLUE, LEXTREME, SCALE) evaluate models on randomly split data, implicitly assuming legal language is stationary. This dataset enables cross-temporal evaluation: train on one epoch, test on another, to measure how model performance degrades over time.

Prior work established a **27.9 percentage-point forward degradation** gap using TF-IDF classifiers on this data. This dataset supports neural experiments to determine whether transformer fine-tuning and legal-domain pretraining mitigate temporal drift.

## Temporal Epochs

| Epoch | Period | Decisions | Context |
|-------|--------|-----------|---------|
| `pre_war` | 2008–2013 | 128,075 | Peacetime. All 832 courts operational, stable procedural rules. |
| `hybrid_war` | 2014–2021 | 150,000 | Crimea annexation (2014), judicial reform (2017), procedural modernization. |
| `full_scale` | 2022–2026 | 150,000 | Full-scale invasion, martial law, new Criminal Code articles, military case surge. |

## Task

**Judgment prediction** (3-class classification): given the facts section of a court decision, predict the outcome.

| Label | Ukrainian | Meaning |
|-------|-----------|---------|
| `approved` | задоволено | Claim fully granted |
| `dismissed` | відмовлено | Claim denied |
| `partial` | частково задоволено | Claim partially granted |

## Dataset Structure

### Configs

- **`all`** (default) -- all 428K decisions combined, chronological split
- **`pre_war`** -- 128,075 decisions from 2008–2013
- **`hybrid_war`** -- 150,000 decisions from 2014–2021
- **`full_scale`** -- 150,000 decisions from 2022–2026

### Splits

Within each config, documents are split **chronologically** (not randomly):
- **train** (80%) -- earliest decisions
- **validation** (10%) -- middle
- **test** (10%) -- most recent decisions

This prevents temporal leakage: models are always evaluated on decisions that postdate their training data.

### Fields

| Field | Type | Description |
|-------|------|-------------|
| `text` | string | Facts section of the court decision (model input). PII replaced with `[PERSON]`, `[ADDRESS]`, `[NUMBER]`, `[INFO]`. Truncated to 10,000 characters. |
| `label` | string | Outcome: `approved`, `dismissed`, or `partial` |
| `epoch` | string | Temporal epoch: `pre_war`, `hybrid_war`, or `full_scale` |
| `adjudication_date` | string | Date of the decision (YYYY-MM-DD) |
| `jurisdiction` | string | `civil` or `commercial` |
| `doc_id` | string | EDRSR document ID (links to https://reyestr.court.gov.ua/) |
| `language` | string | Always `uk` (Ukrainian) |

### Statistics

| Config | Split | Samples | Avg length (chars) |
|--------|-------|--------:|-------------------:|
| pre_war | train | 102,460 | 5,267 |
| pre_war | validation | 12,807 | 4,773 |
| pre_war | test | 12,808 | 5,324 |
| hybrid_war | train | 120,000 | 6,456 |
| hybrid_war | validation | 15,000 | 7,698 |
| hybrid_war | test | 15,000 | 7,783 |
| full_scale | train | 120,000 | 8,068 |
| full_scale | validation | 15,000 | 8,678 |
| full_scale | test | 15,000 | 8,731 |
| **Total** | | **428,075** | |

Note: average text length increases across epochs (5.3K → 8.7K chars), reflecting increasing judicial documentation standards.

## Usage

```python
from datasets import load_dataset

# Load a single epoch
ds = load_dataset("overthelex/ua-temporal-drift", "hybrid_war")

# Cross-epoch evaluation: train on pre_war, test on full_scale
train = load_dataset("overthelex/ua-temporal-drift", "pre_war", split="train")
test = load_dataset("overthelex/ua-temporal-drift", "full_scale", split="test")

# Load all epochs combined
ds = load_dataset("overthelex/ua-temporal-drift", "all")
```

### Cross-Temporal Evaluation Protocol

```python
from datasets import load_dataset
from transformers import AutoModelForSequenceClassification, Trainer

epochs = ["pre_war", "hybrid_war", "full_scale"]

# Train one model per epoch
for train_epoch in epochs:
    train_ds = load_dataset("overthelex/ua-temporal-drift", train_epoch, split="train")
    model = AutoModelForSequenceClassification.from_pretrained("xlm-roberta-base", num_labels=3)
    # ... fine-tune on train_ds ...

    # Evaluate on ALL epochs
    for test_epoch in epochs:
        test_ds = load_dataset("overthelex/ua-temporal-drift", test_epoch, split="test")
        # ... evaluate model on test_ds ...
        # This produces a 3x3 generalization matrix
```

## Source

Decisions are extracted from the **Unified State Register of Court Decisions** (EDRSR / ЄДРСР), a publicly accessible database of all Ukrainian court decisions since 2006 containing over 100 million documents.

- Registry: https://reyestr.court.gov.ua/
- Individual decisions can be accessed at: `https://reyestr.court.gov.ua/Review/{doc_id}`

## Related Datasets

- [overthelex/ukrainian-court-decisions](https://huggingface.co/datasets/overthelex/ukrainian-court-decisions) -- 7-class judgment prediction with 14,452 decisions
- [overthelex/ua-case-outcome](https://huggingface.co/datasets/overthelex/ua-case-outcome) -- case outcome prediction benchmark
- [overthelex/ua-legal-bench](https://huggingface.co/datasets/overthelex/ua-legal-bench) -- LLM evaluation on Ukrainian legal tasks
- [overthelex/ua-statute-retrieval](https://huggingface.co/datasets/overthelex/ua-statute-retrieval) -- statute retrieval benchmark
- [joelniklaus/lextreme](https://huggingface.co/datasets/joelniklaus/lextreme) -- multilingual legal NLP benchmark (submitted as Ukrainian contribution)

## Citation

```bibtex
@misc{ovcharov2025temporaldrift,
  title={Temporal Concept Drift in Legal Judgment Prediction: Neural Baselines Across Three Epochs of Ukrainian Court Decisions},
  author={Ovcharov, Volodymyr},
  year={2025},
  url={https://huggingface.co/datasets/overthelex/ua-temporal-drift},
}
```

## Access

This dataset is **gated**: you must request access and describe your use case. Requests are reviewed manually. Academic and research use is approved by default.

## License

CC-BY-4.0. The source data (EDRSR) is publicly available under Ukrainian open data legislation.