| --- |
| license: apache-2.0 |
| datasets: |
| - BioMike/formal-logic-reasoning-gliclass-2k |
| - knowledgator/gliclass-v3-logic-dataset |
| - tau/commonsense_qa |
| metrics: |
| - f1 |
| tags: |
| - text classification |
| - nli |
| - sentiment analysis |
| pipeline_tag: text-classification |
| language: |
| - en |
| - sv |
| - cs |
| - pl |
| - lt |
| - et |
| - lv |
| - es |
| - fi |
| - de |
| - fr |
| - ro |
| - it |
| - pt |
| - nl |
| - uk |
| - hi |
| - zh |
| - ar |
| - he |
| --- |
| |
|  |
|
|
|  |
|
|
| # GLiClass Multilang: Efficient multilingual zero-shot and few-shot multi-task model via sequence classification |
|
|
| GLiClass is an efficient zero-shot sequence classification model designed to achieve SoTA performance while being much faster than cross-encoders and LLMs, while preserving strong generalization capabilities. |
|
|
| The model supports text classification with any labels and can be used for the following tasks: |
| * Topic Classification |
| * Sentiment Analysis |
| * Intent Classification |
| * Reranking |
| * Hallucination Detection |
| * Rule-following Verification |
| * LLM-safety Classification |
| * Natural Language Inference |
|
|
| ## ✨ What's New in GLiClass Multilang |
| - **Multilingual Training** — Natively trained on 20 languages: Swedish, Norwegian, Czech, Polish, Lithuanian, Estonian, Latvian, Spanish, Finnish, German, French, Romanian, Italian, Portuguese, Dutch, Ukrainian, Hindi, Chinese, Arabic, and Hebrew. |
| - **Cross-lingual Classification** — Labels and input texts can be in different languages; classify a German document with English labels, or mix languages freely across inputs and labels. |
| - **CrossAttn Scorer** — A new cross-attention scorer enables more efficient pooling independently for each label with unpadding and flash-attn. |
| - **Hierarchical Labels** — Organize labels into groups using dot notation or dictionaries (e.g., `sentiment.positive`, `topic.product`). |
| - **Few-Shot Examples** — Provide in-context examples to boost accuracy on your specific task. |
| - **Label Descriptions** — Add natural-language descriptions to labels for more precise classification. |
| - **Task Prompts** — Prepend a custom prompt to guide the model's classification behavior. |
|
|
| See the [GLiClass library README](https://github.com/Knowledgator/GLiClass) for full details on these features. |
|
|
| ## Installation |
|
|
| ```bash |
| pip install gliclass |
| ``` |
|
|
| ## Quick Start |
|
|
| ```python |
| from gliclass import GLiClassModel, ZeroShotClassificationPipeline |
| from transformers import AutoTokenizer |
| |
| model = GLiClassModel.from_pretrained("knowledgator/gliclass-multilang-mini") |
| tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-multilang-mini") |
| pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0') |
| |
| text = "NASA launched a new Mars rover to search for signs of ancient life." |
| labels = ["space", "politics", "sports", "technology", "health"] |
| |
| results = pipeline(text, labels, threshold=0.5)[0] |
| for r in results: |
| print(r["label"], "=>", r["score"]) |
| ``` |
|
|
| --- |
| ## Multilingual & Cross-lingual Capabilities |
|
|
| Natively trained on 20 languages. Labels and texts can be in different languages. |
|
|
| **Same language (German):** |
| ```python |
| from gliclass import GLiClassModel, ZeroShotClassificationPipeline |
| from transformers import AutoTokenizer |
| |
| model = GLiClassModel.from_pretrained("knowledgator/gliclass-multilang-mini") |
| tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-multilang-mini") |
| pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0') |
| |
| text = "Die NASA hat einen neuen Mars-Rover gestartet, um nach Spuren alten Lebens zu suchen." |
| labels = ["Weltraum", "Politik", "Sport", "Technologie", "Gesundheit"] |
| results = pipeline(text, labels, threshold=0.5)[0] |
| for r in results: |
| print(r["label"], "=>", r["score"]) |
| ``` |
|
|
| **Cross-lingual (French text, English labels):** |
| ```python |
| text = "Le gouvernement français a annoncé de nouvelles mesures économiques." |
| labels = ["economy", "politics", "sports", "technology"] |
| results = pipeline(text, labels, threshold=0.5)[0] |
| for r in results: |
| print(r["label"], "=>", r["score"]) |
| ``` |
|
|
| **Cross-lingual (Arabic text, English labels):** |
| ```python |
| text = "أطلقت ناسا مركبة جديدة للمريخ للبحث عن آثار الحياة القديمة." |
| labels = ["space", "politics", "sports", "technology"] |
| results = pipeline(text, labels, threshold=0.5)[0] |
| for r in results: |
| print(r["label"], "=>", r["score"]) |
| ``` |
|
|
| **Cross-lingual (English text, Spanish labels):** |
| ```python |
| text = "NASA launched a new Mars rover to search for signs of ancient life." |
| labels = ["espacio", "política", "deportes", "tecnología", "salud"] |
| results = pipeline(text, labels, threshold=0.5)[0] |
| for r in results: |
| print(r["label"], "=>", r["score"]) |
| ``` |
|
|
| <details> |
| <summary>General Examples</summary> |
|
|
| ### 1. Topic Classification |
|
|
| ```python |
| text = "NASA launched a new Mars rover to search for signs of ancient life." |
| labels = ["space", "politics", "sports", "technology", "health"] |
| |
| results = pipeline(text, labels, threshold=0.5)[0] |
| for r in results: |
| print(r["label"], "=>", r["score"]) |
| ``` |
|
|
| #### With hierarchical labels |
|
|
| ```python |
| hierarchical_labels = { |
| "science": ["space", "biology", "physics"], |
| "society": ["politics", "economics", "culture"] |
| } |
| |
| results = pipeline(text, hierarchical_labels, threshold=0.5)[0] |
| for r in results: |
| print(r["label"], "=>", r["score"]) |
| # e.g. science.space => 0.95 |
| ``` |
|
|
| ### 2. Sentiment Analysis |
|
|
| ```python |
| text = "The food was excellent but the service was painfully slow." |
| labels = ["positive", "negative", "neutral"] |
| |
| results = pipeline(text, labels, threshold=0.5)[0] |
| for r in results: |
| print(r["label"], "=>", r["score"]) |
| ``` |
|
|
| #### With a task prompt |
|
|
| ```python |
| results = pipeline( |
| text, labels, |
| prompt="Classify the sentiment of this restaurant review:", |
| threshold=0.5 |
| )[0] |
| ``` |
|
|
| ### 3. Intent Classification |
|
|
| ```python |
| text = "Can you set an alarm for 7am tomorrow?" |
| labels = ["set_alarm", "play_music", "get_weather", "send_message", "set_reminder"] |
| |
| results = pipeline(text, labels, threshold=0.5)[0] |
| for r in results: |
| print(r["label"], "=>", r["score"]) |
| ``` |
|
|
| ### 4. Natural Language Inference |
|
|
| Represent your premise as the text and the hypothesis as a label. The model works best with a single hypothesis at a time. |
|
|
| ```python |
| text = "The cat slept on the windowsill all afternoon." |
| labels = ["The cat was awake and playing outside."] |
| |
| results = pipeline(text, labels, threshold=0.0)[0] |
| print(results) |
| # Low score → contradiction |
| ``` |
|
|
| ### 5. Reranking |
|
|
| Score query–passage relevance by treating passages as texts and the query as the label: |
|
|
| ```python |
| query = "How to train a neural network?" |
| passages = [ |
| "Backpropagation is the key algorithm for training deep neural networks.", |
| "The stock market rallied on strong earnings reports.", |
| "Gradient descent optimizes model weights during training.", |
| ] |
| |
| for passage in passages: |
| score = pipeline(passage, [query], threshold=0.0)[0][0]["score"] |
| print(f"{score:.3f} {passage[:60]}") |
| ``` |
|
|
| ### 6. Rule-following Verification |
|
|
| Include the domain and rules as part of the text: |
|
|
| ```python |
| text = ( |
| "Domain: e-commerce product reviews\n" |
| "Rule: No promotion of illegal activity.\n" |
| "Text: The software is okay, but search for 'productname_patch_v2.zip' " |
| "to unlock all features for free." |
| ) |
| labels = ["follows_guidelines", "violates_guidelines"] |
| |
| results = pipeline(text, labels, threshold=0.0)[0] |
| for r in results: |
| print(r["label"], "=>", r["score"]) |
| ``` |
|
|
| </details> |
|
|
| --- |
|
|
| ## Benchmarks |
|
|
| ### Model Overview |
|
|
| Summary across all evaluated multilingual-capable models (zero-shot, no fine-tuning). Speed averaged over all label counts and text lengths at batch_size=8 on NVIDIA RTX PRO 6000 Blackwell. |
| |
| | Model | Params | English avg F1 | Multilingual avg F1 | Throughput (samp/s, bs=8) | |
| |---|---:|---:|---:|---:| |
| | [multilang‑ultra](https://huggingface.co/knowledgator/gliclass-multilang-ultra) | ~1 720M | **0.7212** | **0.5599** | 200.7 | |
| | [multilang‑mini](https://huggingface.co/knowledgator/gliclass-multilang-mini) | ~288M | 0.6827 | 0.5378 | **513.4** | |
| | [multilang‑edge](https://huggingface.co/knowledgator/gliclass-multilang-edge) | ~140M | 0.6196 | 0.3959 | **553.6** | |
| | [instruct‑large](https://huggingface.co/knowledgator/gliclass-instruct-large-v1.0) | ~435M | 0.7199 | — | 293.9 | |
| | [instruct‑base](https://huggingface.co/knowledgator/gliclass-instruct-base-v1.0) | ~184M | 0.6525 | — | 521.9 | |
| | [gliner2‑large‑v1](https://huggingface.co/fastino/gliner2-large-v1) | 340M | 0.6774 | — | 122.5 | |
| | [gliner2‑multi‑v1](https://huggingface.co/fastino/gliner2-multi-v1) | ~278M | 0.6387 | 0.4659 | 200.2 | |
| | [gliner2‑base‑v1](https://huggingface.co/fastino/gliner2-base-v1) | ~184M | 0.6336 | — | 224.0 | |
| | [bge‑m3‑zeroshot‑v2.0](https://huggingface.co/MoritzLaurer/bge-m3-zeroshot-v2.0) | 568M | 0.5927 | 0.5225 | 208.7 | |
| | [mDeBERTa‑mnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 300M | 0.5340 | 0.3926 | 160.6 | |
| |
| > Multilingual avg F1 is the mean of 6 dataset-level scores (GermEval2017, MASSIVE, PolygloToxicityPrompts, SIB-200, TextDetox, TweetSentiment). Models without multilingual results (—) were only evaluated on English datasets. |
| |
| --- |
| |
| F1 scores on zero-shot text classification (no fine-tuning on these datasets): |
| |
| **Table A: GLiClass Multilang (macro F1)** |
| |
| | Dataset | [multilang‑ultra](https://huggingface.co/knowledgator/gliclass-multilang-ultra) | [multilang‑mini](https://huggingface.co/knowledgator/gliclass-multilang-mini) | [multilang‑edge](https://huggingface.co/knowledgator/gliclass-multilang-edge) | |
| |---|---|---|---| |
| | CR | 0.9226 | 0.9042 | 0.8852 | |
| | sst2 | 0.9065 | 0.8810 | 0.8276 | |
| | sst5 | 0.3049 | 0.2806 | 0.3047 | |
| | 20_newsgroups | 0.5238 | 0.4242 | 0.3522 | |
| | spam | 0.9625 | 0.9385 | 0.6787 | |
| | financial_phrasebank | 0.8724 | 0.7156 | 0.7446 | |
| | imdb | 0.9330 | 0.9011 | 0.8730 | |
| | ag_news | 0.7454 | 0.7545 | 0.7338 | |
| | emotion | 0.4825 | 0.4655 | 0.4267 | |
| | cap_sotu | 0.4385 | 0.4087 | 0.3516 | |
| | rotten_tomatoes | 0.8413 | 0.8236 | 0.7044 | |
| | massive | 0.6483 | 0.5853 | 0.5649 | |
| | banking | 0.6492 | 0.5853 | 0.5788 | |
| | snips | 0.8653 | 0.8900 | 0.6487 | |
| | **AVERAGE** | **0.7212** | **0.6827** | **0.6196** | |
|
|
| **Table B: Baselines (macro F1)** |
|
|
| | Dataset | [gliner2‑large‑v1](https://huggingface.co/fastino/gliner2-large-v1) | [gliner2‑multi‑v1](https://huggingface.co/fastino/gliner2-multi-v1) | [gliner2‑base‑v1](https://huggingface.co/fastino/gliner2-base-v1) | [bge‑m3‑zeroshot‑v2.0](https://huggingface.co/MoritzLaurer/bge-m3-zeroshot-v2.0) | [mDeBERTa‑mnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | |
| |---|---|---|---|---|---| |
| | CR | 0.9117 | 0.8785 | 0.8783 | 0.9041 | 0.8956 | |
| | sst2 | 0.8911 | 0.8568 | 0.8737 | 0.9257 | 0.8516 | |
| | sst5 | 0.4462 | 0.3784 | 0.4100 | 0.2931 | 0.3023 | |
| | 20_newsgroups | 0.5163 | 0.3668 | 0.4608 | 0.4161 | 0.2080 | |
| | spam | 0.3558 | 0.5986 | 0.3843 | 0.4410 | 0.4980 | |
| | financial_phrasebank | 0.8330 | 0.7372 | 0.7225 | 0.5040 | 0.4444 | |
| | imdb | 0.9170 | 0.8934 | 0.8982 | 0.8730 | 0.8264 | |
| | ag_news | 0.7029 | 0.7403 | 0.7193 | 0.6870 | 0.6547 | |
| | emotion | 0.5233 | 0.4666 | 0.4577 | 0.4530 | 0.4055 | |
| | cap_sotu | 0.4387 | 0.3972 | 0.3831 | 0.4720 | 0.3390 | |
| | rotten_tomatoes | 0.7909 | 0.7210 | 0.6979 | 0.8130 | 0.6931 | |
| | massive | 0.5897 | 0.4721 | 0.5403 | 0.4140 | 0.2527 | |
| | banking | 0.6885 | 0.6390 | 0.6709 | 0.3870 | 0.3796 | |
| | snips | 0.8788 | 0.7954 | 0.7731 | 0.7149 | 0.7245 | |
| | **AVERAGE** | **0.6774** | **0.6387** | **0.6336** | **0.5927** | **0.5340** | |
| |
| **Table C: GLiClass-V1 Multitask (macro F1)** |
| |
| | Dataset | [instruct‑large‑v1.0](https://huggingface.co/knowledgator/gliclass-instruct-large-v1.0) | [instruct‑base‑v1.0](https://huggingface.co/knowledgator/gliclass-instruct-base-v1.0) | [edge‑v1.0](https://huggingface.co/knowledgator/gliclass-instruct-edge-v1.0) | |
| |---|---|---|---| |
| | CR | 0.9066 | 0.8922 | 0.7933 | |
| | sst2 | 0.9154 | 0.9198 | 0.7577 | |
| | sst5 | 0.3387 | 0.2266 | 0.2163 | |
| | 20_newsgroups | 0.5577 | 0.5189 | 0.2555 | |
| | spam | 0.9790 | 0.9380 | 0.7609 | |
| | financial_phrasebank | 0.8289 | 0.5217 | 0.3905 | |
| | imdb | 0.9397 | 0.9364 | 0.8159 | |
| | ag_news | 0.7521 | 0.6978 | 0.6043 | |
| | emotion | 0.4473 | 0.4454 | 0.2941 | |
| | cap_sotu | 0.4327 | 0.4579 | 0.2380 | |
| | rotten_tomatoes | 0.8491 | 0.8458 | 0.5455 | |
| | massive | 0.5824 | 0.4757 | 0.2090 | |
| | banking | 0.6987 | 0.6072 | 0.4635 | |
| | snips | 0.8509 | 0.6515 | 0.5461 | |
| | **AVERAGE** | **0.7199** | **0.6525** | **0.4922** | |
|
|
| ### Multilingual Benchmarks |
|
|
| Macro F1 averaged per dataset across all evaluated languages: |
|
|
| | Dataset | [multilang‑ultra](https://huggingface.co/knowledgator/gliclass-multilang-ultra) | [multilang‑mini](https://huggingface.co/knowledgator/gliclass-multilang-mini) | [multilang‑edge](https://huggingface.co/knowledgator/gliclass-multilang-edge) | [gliner2‑multi‑v1](https://huggingface.co/fastino/gliner2-multi-v1) | [bge‑m3‑zeroshot‑v2.0](https://huggingface.co/MoritzLaurer/bge-m3-zeroshot-v2.0) | [mDeBERTa‑mnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | |
| |---|---|---|---|---|---|---| |
| | germeval2017 | 0.4647 | 0.4826 | 0.4094 | 0.4223 | 0.4503 | 0.2849 | |
| | massive | 0.5635 | 0.4925 | 0.2853 | 0.3625 | 0.4646 | 0.2427 | |
| | polyglot_toxicity | 0.7367 | 0.7110 | 0.4474 | 0.6630 | 0.6809 | 0.5698 | |
| | sib200 | 0.1935 | 0.1921 | 0.1492 | 0.1750 | 0.1891 | 0.1476 | |
| | textdetox | 0.7428 | 0.7313 | 0.5811 | 0.5912 | 0.7510 | 0.6490 | |
| | tweet_sentiment | 0.6579 | 0.6171 | 0.5030 | 0.5814 | 0.5991 | 0.4615 | |
| | **AVERAGE** | **0.5599** | **0.5378** | **0.3959** | **0.4659** | **0.5225** | **0.3926** | |
|
|
| Per-language macro F1 (16-language fair comparison on massive + sib200): |
|
|
| | Language | [multilang‑ultra](https://huggingface.co/knowledgator/gliclass-multilang-ultra) | [multilang‑mini](https://huggingface.co/knowledgator/gliclass-multilang-mini) | [multilang‑edge](https://huggingface.co/knowledgator/gliclass-multilang-edge) | [gliner2‑multi‑v1](https://huggingface.co/fastino/gliner2-multi-v1) | [bge‑m3‑zeroshot‑v2.0](https://huggingface.co/MoritzLaurer/bge-m3-zeroshot-v2.0) | [mDeBERTa‑mnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | |
| |---|---|---|---|---|---|---| |
| | arabic | 0.3210 | 0.3043 | 0.1843 | 0.2394 | 0.2862 | 0.1567 | |
| | chinese | 0.3888 | 0.3636 | 0.2724 | 0.2947 | 0.3459 | 0.2356 | |
| | dutch | 0.3949 | 0.3587 | 0.2660 | 0.2828 | 0.3284 | 0.2146 | |
| | finnish | 0.3632 | 0.3174 | 0.1172 | 0.2704 | 0.3357 | 0.1884 | |
| | french | 0.3965 | 0.3679 | 0.2963 | 0.2946 | 0.3396 | 0.1978 | |
| | german | 0.3654 | 0.3457 | 0.2532 | 0.2767 | 0.3164 | 0.1966 | |
| | hebrew | 0.3521 | 0.3206 | 0.1271 | 0.2641 | 0.3287 | 0.1796 | |
| | hindi | 0.3934 | 0.3529 | 0.1877 | 0.0817 | 0.3240 | 0.1986 | |
| | italian | 0.3919 | 0.3474 | 0.2604 | 0.2891 | 0.3146 | 0.1976 | |
| | latvian | 0.3643 | 0.3165 | 0.1205 | 0.2741 | 0.3163 | 0.1774 | |
| | norwegian | 0.3770 | 0.3489 | 0.2043 | 0.2803 | 0.3382 | 0.1965 | |
| | polish | 0.3961 | 0.3577 | 0.2112 | 0.2814 | 0.3225 | 0.1981 | |
| | portuguese | 0.4008 | 0.3482 | 0.2798 | 0.3057 | 0.3346 | 0.1936 | |
| | romanian | 0.3740 | 0.3204 | 0.2210 | 0.2831 | 0.3291 | 0.1944 | |
| | spanish | 0.3921 | 0.3535 | 0.2905 | 0.2924 | 0.3371 | 0.1918 | |
| | swedish | 0.3863 | 0.3547 | 0.2121 | 0.2799 | 0.3317 | 0.2019 | |
| | **AVERAGE** | **0.3786** | **0.3424** | **0.2190** | **0.2681** | **0.3268** | **0.1950** | |
|
|
| ## Throughput |
|
|
|  |
|
|
| Throughput (samples/sec), batch_size=8, GPU: NVIDIA RTX PRO 6000 Blackwell. Averaged over text lengths (64 / 256 / 512 tokens). |
| |
| | Model | 1 label | 2 | 4 | 8 | 16 | 32 | 64 | 128 | 256 | **avg** | |
| |---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| |
| | [multilang‑ultra](https://huggingface.co/knowledgator/gliclass-multilang-ultra) | 308.2 | 302.5 | 281.8 | 266.3 | 235.9 | 190.5 | 125.2 | 64.7 | 31.5 | **200.7** | |
| | [multilang‑mini](https://huggingface.co/knowledgator/gliclass-multilang-mini) | 708.4 | 703.9 | 692.5 | 664.2 | 618.1 | 518.1 | 396.1 | 221.2 | 98.2 | **513.4** | |
| | [multilang‑edge](https://huggingface.co/knowledgator/gliclass-multilang-edge) | 697.0 | 699.7 | 689.5 | 671.0 | 637.7 | 553.3 | 469.8 | 345.2 | 219.2 | **553.6** | |
| | [instruct‑large](https://huggingface.co/knowledgator/gliclass-instruct-large-v1.0) | 397.2 | 393.1 | 386.6 | 374.2 | 351.1 | 313.3 | 223.8 | 142.2 | 63.2 | **293.9** | |
| | [instruct‑base](https://huggingface.co/knowledgator/gliclass-instruct-base-v1.0) | 708.0 | 707.5 | 693.5 | 666.4 | 616.7 | 526.5 | 405.5 | 248.1 | 124.9 | **521.9** | |
| | [gliner2‑large‑v1](https://huggingface.co/fastino/gliner2-large-v1) | 165.6 | 165.2 | 157.1 | 155.6 | 142.1 | 122.1 | 98.6 | 65.6 | 31.0 | **122.5** | |
| | [gliner2‑multi‑v1](https://huggingface.co/fastino/gliner2-multi-v1) | 270.4 | 267.9 | 264.6 | 257.3 | 237.2 | 200.0 | 159.2 | 96.8 | 48.4 | **200.2** | |
| | [gliner2‑base‑v1](https://huggingface.co/fastino/gliner2-base-v1) | 296.8 | 293.2 | 287.8 | 278.9 | 262.0 | 229.4 | 180.1 | 121.3 | 66.2 | **224.0** | |
| | [bge‑m3‑zeroshot‑v2.0](https://huggingface.co/MoritzLaurer/bge-m3-zeroshot-v2.0) | 940.0 | 474.7 | 238.4 | 112.9 | 58.3 | 28.9 | 14.4 | 7.2 | 3.7 | **208.7** | |
| | [mDeBERTa‑mnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 717.5 | 364.5 | 183.1 | 91.8 | 45.7 | 22.8 | 11.4 | 5.7 | 3.0 | **160.6** | |
| |
| > NLI models (bge-m3, mDeBERTa) run one forward pass per label — throughput drops linearly with label count. GLiClass and GLiNER2 encode all labels in a single pass, so throughput stays nearly flat. |
| |
| ## Citation |
| |
| ```bibtex |
| @misc{stepanov2025gliclassgeneralistlightweightmodel, |
| title={GLiClass: Generalist Lightweight Model for Sequence Classification Tasks}, |
| author={Ihor Stepanov and Mykhailo Shtopko and Dmytro Vodianytskyi and Oleksandr Lukashov and Alexander Yavorskyi and Mykyta Yaroshenko}, |
| year={2025}, |
| eprint={2508.07662}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG}, |
| url={https://arxiv.org/abs/2508.07662}, |
| } |
| ``` |
