EttinX-nli-s
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Explore AI models 2428
EttinX-sts-xxs
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View details ↗ModernCE-base-nli
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View details ↗ModernCE-base-sts
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View details ↗ModernCE-large-nli
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View details ↗ModernCE-large-sts
dleemiller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 ↗deberta-v3-large-finetuned-paws-paraphrase-detector
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View details ↗deberta-v3-xsmall-beavertails-harmful-qa-classifier
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View details ↗legal-bert-ner-base-cased-ptbr
dominguesm**ner-legal-bert-base-cased-ptbr** is a NER model (token classification) in the legal domain in Portuguese that was finetuned from the model dominguesm/legal-bert-base-cased-ptbr by using a NER objective. The model is intended to assist NLP research in the legal field, computer law and legal technology applications. Several legal texts in Portuguese (more information below) were used with the following labels: The labels were inspired by the LeNER_br dataset.
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View details ↗ko-reranker
Dongjin-krko-reranker는 BAAI/bge-reranker-larger 기반 한국어 데이터에 대한 fine-tuned model 입니다. 보다 자세한 사항은 korean-reranker-git / AWS Blog, 한국어 Reranker를 활용한 검색 증강 생성(RAG) 성능 올리기을 참고하세요 - #### Reranker는 임베딩 모델과 달리 질문과 문서를 입력으로 사용하며 임베딩 대신 유사도를 직접 출력합니다. - #### Reranker에 질문과 구절을 입력하면 연관성 점수를 얻을 수 있습니다. - #### Reranker는 CrossEntropy loss를 기반으로 최적화되므로 관련성 점수가 특정 범위에 국한되지 않습니다. - #### **컨택스트 순서가 정확도에 영향 준다**(Lost in Middle, *Liu et al., 2023*)
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View details ↗THX-01
doofz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 ↗RoBERTa-tl-sentiment-analysis
dost-asti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 ↗bert-base-uncased-finetuned-sst2
doyoungkim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 ↗vibert-capu
dragonSwing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 ↗NeoHorse-Jev-4B-GGUF
DreamBlooms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 ↗Tev1-0.8B-experimental-GGUF
DreamBlooms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 ↗bert-large-NER
dslimIf my open source models have been useful to you, please consider supporting me in building small, useful AI models for everyone (and help me afford med school / help out my parents financially). Thanks! **bert-large-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). Specifically, this model is a *bert-large-cased* model that was fine-tuned on the English version of the standard CoNLL-2003 Named Entity Recognition dataset.
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View details ↗distilbert-NER
dslimIf my open source models have been useful to you, please consider supporting me in building small, useful AI models for everyone (and help me afford med school / help out my parents financially). Thanks! **distilbert-NER** is the fine-tuned version of **DistilBERT**, which is a distilled variant of the BERT model. DistilBERT has fewer parameters than BERT, making it smaller, faster, and more efficient. distilbert-NER is specifically fine-tuned for the task of **Named Entity Recognition (NER)**. This model accurately identifies the same four types of entities as its BERT counterparts: location (LOC), organizations (ORG), person (PER), and Miscellaneous (MISC). Although it is a more compact model, distilbert-NER demonstrates a robust performance in NER tasks, balancing between size, speed, and accuracy.
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View details ↗This model is a fine-tuned version of roberta-base on the NYT News dataset, which contains 256,000 news titles from articles published from 2000 to the present (https://www.kaggle.com/datasets/aryansingh0909/nyt-articles-21m-2000-present). It achieves the following results on the test set of 51200 cases: - Accuracy: 0.91 - F1: 0.91 - Precision: 0.91 - Recall: 0.91 class |Description -|- 0 |Sports 1 |Arts, Culture, and Entertainment 2 |Business and Finance 3 |Health and Wellness 4 |Lifestyle and Fashion 5 |Science and Technology 6 |Politics 7 |Crime The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5
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View details ↗log-anomaly-detection-model-new
Dumi2025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 ↗bert-base-romanian-ner
dumitrescustefan**bert-base-romanian-ner** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance** for the NER task. It has been trained to recognize **15** types of entities: persons, geo-political entities, locations, organizations, languages, national_religious_political entities, datetime, period, quantity, money, numeric, ordinal, facilities, works of art and events. Specifically, this model is a bert-base-romanian-cased-v1 model that was fine-tuned on RONEC version 2.0, which holds 12330 sentences with over 0.5M tokens, to a total of 80.283 distinctly annotated entities. RONECv2 is a BIO2 annotated corpus, meaning this model will generate "B-" and "I-" style labels for entities. The model will generate labels according to the following list: ['O', 'B-PERSON', 'I-PERSON', 'B-ORG', 'I-ORG', 'B-GPE', 'I-GPE', 'B-LOC', 'I-LOC', 'B-NAT_REL_POL', 'I-NAT_REL_POL', 'B-EVENT', 'I-EVENT', 'B-LANGUAGE', 'I-LANGUAGE', 'B-WORK_OF_ART', 'I-WORK_OF_ART', 'B-DATETIME', 'I-DATETIME', 'B-PERIOD', 'I-PERIOD', 'B-MONEY', 'I-MONEY', 'B-QUANTITY', 'I-QUANTITY', 'B-NUMERIC', 'I-NUMERIC', 'B-ORDINAL', 'I-ORDINAL', 'B-FACILITY', 'I-FACILITY']. Label 'O' represents Other.
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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 ↗Dymium-NER-v1
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