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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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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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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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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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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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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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查看详情 ↗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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查看详情 ↗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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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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。