shingi-27b
kortexa-ai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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answer-equivalence-bem
kortukov基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗gutsy
kouhxp基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗KPF-bert-ner
KPF- 빅카인즈랩 인사이드 메뉴의 개체명 분석에서 사용된 개체명 인식 모델이다. - 사용 방법에 대한 안내 및 코드는 KPF-bigkinds github에서 확인할 수 있습니다. 한국언론진흥재단이 개발한 kpf-BERT 모델을 기반으로 NER(Named Entity Recognition) task를 수행할 수 있는 kpf-BERT-ner 모델을 설계 및 개발한다. NER은 이름을 가진 객체를 인식하는 것을 의미한다. 한국정보통신기술협회가 제공하는 정보통신용어사전에 따르면 NER은 다음과 같다. “NER은 미리 정의해둔 사람, 회사, 장소, 시간, 단위 등에 해당하는 단어(개체명)를 문서에서 인식하여 추출 분류하는 기법. 추출된 개체명은 인명(person), 지명(location), 기관명(organization), 시간(time) 등으로 분류된다. 개체명 인식은 정보 추출을 목적으로 시작되어 자연어 처리, 정보 검색 등에 사용된다.” 실무적으로 표현하면 ‘문자열을 입력으로 받아 단어별로 해당하는 태그를 출력하게 하는 multi-class 분류 작업’이다. 본 과제에서는 kpf-BERT-ner 모델을 설계 및 개발하고 언론 기사를 학습하여 150개 클래스를 분류한다. - 한국어 데이터 셋은 모두의 말뭉치에서 제공되는 국립국어원 신문 말뭉치 추출 를 사용하였다.
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View details ↗punctuate-all
kredorThis is based on Oliver Guhr's work. The difference is that it is a finetuned xlm-roberta-base instead of an xlm-roberta-large and on twelve languages instead of four. The languages are: English, German, French, Spanish, Bulgarian, Italian, Polish, Dutch, Czech, Portugese, Slovak, Slovenian. 0 0.99 0.99 0.99 73317475 . 0.94 0.95 0.95 4484845 , 0.86 0.86 0.86 6100650 ? 0.88 0.85 0.86 136479 - 0.60 0.29 0.39 233630 : 0.71 0.49 0.58 152424 accuracy 0.98 84425503 macro avg 0.83 0.74 0.77 84425503 weighted avg 0.98 0.98 0.98 84425503
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View details ↗banano-kirtis-v2
kristijonas基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗lettucedect-610m-eurobert-de-v1
KRLabsOrg基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗lettucedect-base-modernbert-en-v1
KRLabsOrg**Model Name:** lettucedect-base-modernbert-en-v1 **Organization:** KRLabsOrg **Github:** https://github.com/KRLabsOrg/LettuceDetect LettuceDetect is a transformer-based model for hallucination detection on context and answer pairs, designed for Retrieval-Augmented Generation (RAG) applications. This model is built on **ModernBERT**, which has been specifically chosen and trained becasue of its extended context support (up to **8192 tokens**). This long-context capability is critical for tasks where detailed and extensive documents need to be processed to accurately determine if an answer is supported by the provided context. The model is trained to identify tokens in the answer text that are not supported by the given context. During inference, the model returns token-level predictions which are then aggregated into spans. This allows users to see exactly which parts of the answer are considered hallucinated.
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View details ↗lettucedect-large-modernbert-en-v1
KRLabsOrg**Model Name:** lettucedect-large-modernbert-en-v1 **Organization:** KRLabsOrg **Github:** https://github.com/KRLabsOrg/LettuceDetect LettuceDetect is a transformer-based model for hallucination detection on context and answer pairs, designed for Retrieval-Augmented Generation (RAG) applications. This model is built on **ModernBERT**, which has been specifically chosen and trained becasue of its extended context support (up to **8192 tokens**). This long-context capability is critical for tasks where detailed and extensive documents need to be processed to accurately determine if an answer is supported by the provided context. The model is trained to identify tokens in the answer text that are not supported by the given context. During inference, the model returns token-level predictions which are then aggregated into spans. This allows users to see exactly which parts of the answer are considered hallucinated.
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View details ↗lettucedect-v2-mmbert-base
KRLabsOrg*LettuceDetect goes agentic — span-level hallucination detection across RAG, code, and tool output.* **Paper:** Beyond Document Grounding: Span-Level Hallucination Detection over Code, Tool Output, and Documents (GroundLM 2026 workshop at EMNLP 2026) · arXiv:2607.00895 A **fast encoder** (binary token classifier) for span-level hallucination detection across **code, tool output, and prose**, single forward pass:
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View details ↗tinylettuce-ettin-68m-en
KRLabsOrg基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗verbatim-rag-modern-bert-v2
KRLabsOrg基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗skillscan-deberta-adapter
kurtpayne基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗bigbird-roberta-base-mnli
l-yohai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗clap-htsat-unfused
laion0. TL;DR 1. Model Details 2. Usage 3. Uses 4. Citation > Contrastive learning has shown remarkable success in the field of multimodal representation learning. In this paper, we propose a pipeline of contrastive language-audio pretraining to develop an audio representation by combining audio data with natural language descriptions. To accomplish this target, we first release LAION-Audio-630K, a large collection of 633,526 audio-text pairs from different data sources. Second, we construct a contrastive language-audio pretraining model by considering different audio encoders and text encoders. We incorporate the feature fusion mechanism and keyword-to-caption augmentation into the model design to further enable the model to process audio inputs of variable lengths and enhance the performance. Third, we perform comprehensive experiments to evaluate our model across three tasks: text-to-audio retrieval, zero-shot audio classification, and supervised audio classification. The results demonstrate that our model achieves superior performance in text-to-audio retrieval task. In audio classification tasks, the model achieves state-of-the-art performance in the zero-shot setting and is able to obtain performance comparable to models' results in the non-zero-shot setting. LAION-Audio-630K and the proposed model are both available to the public. You can use this model for zero shot audio classification or extracting audio and/or textual features.
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View details ↗deberta_finetuned_pii
lakshyakh93基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗CodeScaler-8B
LARK-LabWe propose **CodeScaler**, an execution-free reward model designed to scale both reinforcement learning training and test-time inference for code generation. **CodeScaler** is trained on carefully curated preference data derived from verified code problems and incorporates syntax-aware code extraction and validity-preserving reward shaping to ensure stable and robust optimization. This model is the official CodeScaler-8B trained from Skywork/Skywork-Reward-V2-Qwen3-8B on LARK-Lab/CodeScalerPair-51K.
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View details ↗senda
larskjeldgaard基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Wayra-Perplexity-Estimator-55M
latam-gpt基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗la_core_web_lg
latincy**la_core_web_lg**: Latin NLP pipeline for spaCy. Trained on harmonized Universal Dependencies treebanks (Perseus, PROIEL, ITTB, LLCT, UDante, CIRCSE) with LASLA data. Includes POS tagging, morphological analysis, lemmatization, dependency parsing, named entity recognition, and sentence segmentation. « Catus » — from "De cato seu fele" in Conrad Gesner's Historiae animalium liber primus de quadrupedibus viviparis (1551), p. 318 This is a maintenance release across the LatinCy pipeline family (`la_core_web_sm`/`md`/`lg`/`trf`). Model weights are **unchanged** from v3.9.x — no retraining. It also carries the 3.9.7 fix, which was built but never published. Evaluation metrics are not re-measured: the current test set (treebanks 3.10.13) differs from the one these weights were scored on, so the `meta.json` figures remain the v3.9 measurement.
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