datalyes/class_nli_oldnew

Patent NLI (New/Old Classification)

Dataset Description

This dataset is part of PatenTEB, a comprehensive benchmark for evaluating text embedding models on patent-specific tasks. PatenTEB comprises 15 tasks across retrieval, classification, paraphrase detection, and clustering, with 2.06 million examples designed to reflect real-world patent analysis workflows.

Paper: PatenTEB: A Comprehensive Benchmark and Model Family for Patent Text Embedding

Task Details

• Task Name: class_nli_oldnew
• Task Type: Classification
• Test Samples: 14,690
• Training Samples: 23,215 (20% training subset for evaluation)

Binary classification on citation pairs predicting citation directionality: whether the query cites the target (newer→older) or vice versa. This tests whether embeddings capture citation directionality signals and temporal relationships between patents.

Dataset Structure

This is a text classification task.

Splits:

• test: Full test set for evaluation
• train: 20% subset of training data used during evaluation

Columns:

• first_ipcr3
• label
• q
• t
• q_text
• t_text
• first_ipcr3_count

Data Sample

Below is a 5-row preview of the test set:

first_ipcr3,label,q,t,q_text,t_text,first_ipcr3_count
A01,newer,198-768-568-475-930,179-777-815-395-264,"tartaric acid diesters as biodegradable surfactants [SEP] provide water-based compositions having reduced equilibrium and dynamic surface tension, ...","aqueous alcoholic dispersions of ph sensitive polymers and plasticizing agents, a process for preparing the same, and a solid medicinal oral dosage...",1
A01,newer,096-594-257-679-21X,114-925-071-883-209,"bactericidal efficacy improving agent [SEP] obtain a bactericidal composition, and to provide a method for using the bactericidal composition, for ...",starburst conjugates [SEP] starburst conjugates which are composed of at least one dendrimer in association with at least one unit of a carried agr...,1
A01,newer,083-233-929-887-308,172-268-129-338-753,antiulcer agent [SEP] provide a compound having strong therapeutic effect or prophylactic effect to ulcer such as alcoholic ulcer or stress ulcer. ...,"antioxidative compound, method of extracting same from rosemary, and use of same [SEP] the invention provides a novel antioxidant obtained from ros...",1
A01,newer,134-507-658-631-477,096-869-920-872-092,"5-cyan0-4- (pyrrolo [2, 3b] pyridine-3-yl) -pyrimidine derivatives useful as protein kinase inhibitors [SEP] provide a medicament for treating prot...",drug delivery system making use of a hydrogel polymer coating [SEP] the invention features a catheter and methods for delivering drug to tissue at ...,1
A01,newer,186-144-420-300-332,148-664-553-156-857,"methods and compositions for extraction and storage of nucleic acids [SEP] provide solid matrices for extraction, stabilization and storage of nucl...",composition for cleaning drain clogged with deposit containing hair [SEP] composition for cleaning drains clogged with deposits containing hair abs...,1

Evaluation Metrics

This task uses Macro-F1 as the primary metric, computed by averaging per-class F1 scores. Models are evaluated using a 20% training subset with logistic regression classifiers trained on embeddings.

Usage

Load Dataset

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("datalyes/{task_name}")

# Access test split
test_data = dataset['test']

# Access 20% training subset (for classification tasks)
train_data = dataset['train']

Use with Sentence Transformers

from sentence_transformers import SentenceTransformer

# Load a patent-specialized model
model = SentenceTransformer("datalyes/patembed-base")

# Encode patent texts
embeddings = model.encode(test_data['text'])

Integrate with MTEB

This dataset is designed to be integrated with the MTEB (Massive Text Embedding Benchmark) framework. Integration with MTEB is in progress and will be available once the corresponding pull requests are accepted.

Benchmark Context

This dataset is part of a larger benchmark suite:

Benchmark Component	Description
PatenTEB	15 tasks covering retrieval, classification, paraphrase, clustering
Test Data (Released)	319,320 examples across all 15 tasks
Training/Validation Data	1.74 million examples (planned for future release)
Total Dataset Size	2.06 million annotated instances

Note: Currently, only the test split is publicly available. Training and validation data release is planned for a future date.

All 15 Tasks (NEW to MTEB):

• 3 classification tasks: Bloom timing, NLI directionality, IPC3 classification
• 2 clustering tasks: IPC-based, Inventor-based
• 8 retrieval tasks: 3 symmetric (IN/MIXED/OUT domain) + 5 asymmetric (fragment-to-full)
• 2 paraphrase tasks: Problem and solution paraphrase detection

MTEB Integration: Upcoming (PR in progress)

Citation

If you use this dataset, please cite our paper:

@misc{ayaou2025patentebcomprehensivebenchmarkmodel,
      title={PatenTEB: A Comprehensive Benchmark and Model Family for Patent Text Embedding}, 
      author={Iliass Ayaou and Denis Cavallucci},
      year={2025},
      eprint={2510.22264},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2510.22264}
}

License

This dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

• You are free to share and adapt the material
• You must give appropriate credit
• You may not use the material for commercial purposes
• If you remix, transform, or build upon the material, you must distribute your contributions under the same license

For full license details, see: https://creativecommons.org/licenses/by-nc-sa/4.0/

Contact

• Authors: Iliass Ayaou, Denis Cavallucci
• Institution: ICUBE Laboratory, INSA Strasbourg
• GitHub: github.com/iliass-y/patenteb
• HuggingFace: huggingface.co/datalyes