distilbert-base-turkish-cased-offensive
Overfit-GM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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Explore AI models 2428
tmr-ai-text-detector
OxidaneA robust AI-generated text detector based on RoBERTa-base, trained with Focal Loss and Self-Hard-Negative iterative mining on the RAID dataset. TMR (Target Mining RoBERTa) is designed to detect AI-generated text with high accuracy while maintaining low false positive rates. The model uses: Trained on the RAID dataset (ACL 2024) with Self-Hard-Negative mining: iteratively identifying human samples misclassified as AI, then retraining with these hard examples.
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View details ↗zh-wiki-punctuation-restore
p208p2002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 ↗gbert-legal-ner
PaDaS-Lab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 ↗Gender-Classification
padmajabfrlThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Accuracy: 1.0 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5
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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 ↗This model is a fine-tuned version of xlm-roberta-base on the Language Identification dataset. This model is an XLM-RoBERTa transformer model with a classification head on top (i.e. a linear layer on top of the pooled output). For additional information please refer to the xlm-roberta-base model card or to the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al. You can directly use this model as a language detector, i.e. for sequence classification tasks. Currently, it supports the following 20 languages:
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View details ↗PIILOT_NER_Model
ParkJunSeongThis model is a fine-tuned version of monologg/koelectra-base-v3-discriminator for **Personally Identifiable Information (PII) Detection** in Korean text. 이 모델은 한국어 텍스트 내에서 개인정보(이름, 주민등록번호, 전화번호, 이메일 등)를 식별하기 위해 KoELECTRA를 기반으로 미세조정(Fine-tuning)되었습니다. - **Developed by:** ParkJunSeong - **Shared by:** ParkJunSeong - **Language(s):** Korean - **License:** Apache-2.0 - **Base model:** monologg/koelectra-base-v3-discriminator - **Task:** Token Classification (NER)
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View details ↗profanity_model2.0
parsawar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 ↗BERTicelli
patrickquickBERTicelli is an English pre-trained BERT model obtained by fine-tuning the English BERT base cased model with the training data from Offensive Language Identification Dataset (OLID). This model was developed for the NLP Shared Task in the Digital Text Analysis program at the University of Antwerp (2021–2022).
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View details ↗bert-base-japanese-v2-wrime-fine-tune
patrickramos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 ↗lion-warden-ai-security-classifier
patronus-studio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 ↗wolf-defender-jev
patronus-studio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 ↗wolf-defender-prompt-injection
patronus-studioExplore the integrations above, or see how Personaxis, Wanaku and Mygentic Cello integrate Wolf Defender. Wolf Defender is an open, multilingual classifier that detects prompt injection and jailbreak-style instructions before they reach your LLM. Use it to screen user inputs, retrieved documents, emails, web pages and agent tool outputs, with inference running on your own hardware. All models below were evaluated with the same threshold (`0.5`) and document-scoring protocol: 2,048-token windows, 64-token overlap and normalized Smooth-Max aggregation.
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View details ↗wolf-defender-prompt-injection-small
patronus-studioIntegrations across the Wolf Defender model family. AI-Parrot uses Wolf Defender Small as its default ONNX detector. Explore the integrations above, or see how Personaxis, Wanaku and Mygentic Cello integrate Wolf Defender. Wolf Defender Small is an open, multilingual classifier that detects prompt injection and jailbreak-style instructions before they reach your LLM. Its compact ~141M-parameter architecture is designed for local and on-device deployment. Use it to screen user inputs, retrieved documents, emails, web pages and agent tool outputs, with inference running on your own hardware.
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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 ↗mdeberta-v3-ud-thai-pud-upos
Pavarissy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 ↗wangchanberta-ud-thai-pud-upos
Pavarissy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 ↗herbert-base-ner
pczarnik**herbert-base-ner** is a fine-tuned HerBERT model that can be used for **Named Entity Recognition** . It has been trained to recognize three types of entities: person (PER), location (LOC) and organization (ORG). Specifically, this model is an *allegro/herbert-base-cased* model that was fine-tuned on the Polish subset of *wikiann* dataset.
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View details ↗url-relevance
PDAP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 ↗robbert-v2-dutch-ner
pdelobelle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 ↗tiny-random-BertForSequenceClassification
peft-internal-testingThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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View details ↗tiny-random-RobertaForSequenceClassification
peft-internal-testingThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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View details ↗KnowLine-35B-A3B-Gen1.5
PelaAI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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