FOMC-RoBERTa
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 2428
ModernGuard-1
guardion> TL;DR: A state-of-the-art multilingual prompt injection classifier achieving 94.3% F1 score with industry-leading 30ms latency, 20x faster than cloud alternatives, trained on a large corpus of attacks, capable of detecting both explicitly malicious prompts as well as data that contains injected inputs. ModernGuard-1 is a state-of-the-art, cheaper and faster multilingual classifier designed by GuardionAI for real-time detection of prompt injections and jailbreak attempts. Built on the ModernBERT architecture, the model provides industry-leading latency for securing LLM-based applications across over 1,080 languages, making it one of the fastest and most linguistically diverse safety classifiers available. **Context Window:** Supports up to 8,192 tokens (2,048 tokens recommended for optimal real-time performance)
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查看详情 ↗finetuned_nli_provenance
GuardrailsAI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 ↗prompt-saturation-attack-detector
GuardrailsAIA small model to detect saturation jailbreak attacks. Not intended for standalone use against other kinds of jailbreaks. - **Developed by:** Guardrails AI, Joseph Catrambone - **Funded by [optional]:** Guardrails AI - **Model type:** Transformer, BERT - **Language(s) (NLP):** English - **License:** Restrictive - **Finetuned from model [optional]:** bert-tiny Not designed to catch other types of jailbreaks. Saturation protection is one part of a more complite suite of defenses against improper use of ML systems.
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查看详情 ↗nuner-v1_orgs
guishe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 ↗This is a SpanMarker model trained on the FewNERD dataset that can be used for Named Entity Recognition. This SpanMarker model uses numind/generic-entity_recognition_NER-v1 as the underlying encoder.
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查看详情 ↗A finetuned model designed to recognize and classify Personally Identifiable Information (PII) within unstructured text data. This powerful model accurately identifies a wide range of PII categories, such as account names, credit card numbers, emails, phone numbers, and addresses. The model is specifically trained to detect various PII types, including but not limited to: The PII Identifier Model ensures data privacy and compliance by effectively detecting and categorizing sensitive information within documents, emails, user-generated content, and more. Make your data processing safer and more secure with our state-of-the-art PII detection technology.
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查看详情 ↗layoutreader
hantianA reading order prediction model. Turn bboxes extracted from PDF or detected by OCR into readable order. Please refer to Github for more details.
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查看详情 ↗bert-based-false-positive-secrets
harshvkarn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 ↗ams_non_ams_classifier
Harvinder6766This 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 ↗news_sentiment_sentence_v1
Harvinder6766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 ↗bert-base-uncased-hatexplain
Hate-speech-CNERG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 ↗dehatebert-mono-english
Hate-speech-CNERG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 ↗indic-abusive-allInOne-MuRIL
Hate-speech-CNERG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 ↗arabic-ner
hatmimohaThe training corpus is made of 378.000 tokens (14.000 sentences) collected from the Web and annotated manually.
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查看详情 ↗dslim-bert-base-NER-ov-fp32
helenai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 ↗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 ↗english-sarcasm-detector
helinivan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 ↗chatgpt-detector-roberta
Hello-SimpleAIThis model is trained on **the mix of full-text and splitted sentences** of `answer`s from Hello-SimpleAI/HC3. More details refer to arxiv: 2301.07597 and Gtihub project Hello-SimpleAI/chatgpt-comparison-detection. The base checkpoint is roberta-base. We train it with all Hello-SimpleAI/HC3 data (without held-out) for 1 epoch.
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查看详情 ↗chatgpt-detector-roberta-chinese
Hello-SimpleAIThis model is trained on **the mix of full-text and splitted sentences** of `answer`s from Hello-SimpleAI/HC3-Chinese. More details refer to arxiv: 2301.07597 and Gtihub project Hello-SimpleAI/chatgpt-comparison-detection. The base checkpoint is hfl/chinese-roberta-wwm-ext. We train it with all Hello-SimpleAI/HC3-Chinese data (without held-out) for 2 epochs.
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查看详情 ↗rlcd-modernbert-151m
heman10x**OpenJev (Verdict)** is an open-source, post-trained foundational decision model built for structured software workflows, inspired by **TypeSafe AI's Jev** and **Reinforcement Learning for Calibrated Decisions (RLCD)**. It provides calibrated semantic judgments (discrete choices, ordinal scores, and binary probabilities) in a single forward pass without conversational text generation. - **GitHub Repository**: https://github.com/Heman10x-NGU/Verdict-open-jev - **Base Architecture**: ModernBERT-base (`knowledgator/gliclass-modern-base-v2.0`, 151,378,177 parameters) - **Logit Capacity**: 25 candidate slots (24 substantive options + 1 explicit abstention slot) 1. **Non-Autoregressive Single Pass**: Evaluates all candidate options simultaneously in a single forward pass (< 35ms latency) without token generation loops. 2. **Proper Scoring Calibration**: Trained with composite Cross-Entropy + Brier Score loss: $$\mathcal{L}_{\text{total}} = \mathcal{L}_{\text{CE}} + 1.0 \times \mathcal{L}_{\text{Brier}}$$ followed by post-hoc L-BFGS temperature scaling ($T = 1.0716$). 3. **Explicit Abstention Route**: Dedicated `__insufficient_evidence__` candidate slot ensures calibrated rejection on out-of-distribution or insufficient context queries. 4. **Edge and In-Browser WebGPU**: Runs locally in browsers via WebGPU/WASM and on servers via PyTorch/ONNX Runtime.
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查看详情 ↗tiny-random-RobertaForSequenceClassification
hf-tiny-model-private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 ↗ncii-guard-v02
hfmlsocA binary classifier that flags image-editing prompts seeking non-consensual intimate imagery (NCII): requests to strip, undress, or otherwise sexualise a person in a photograph. It is `microsoft/harrier-oss-v1-270m` with a LoRA adapter (r=8) merged in, so it loads with plain `transformers` and needs no `peft`. The unmerged adapter is kept under `adapter/`. **This model does not ship a recommended threshold, and the default 0.5 is not a neutral choice.** Precision on clean text runs from 0.870 at 0.5 to 0.951 at 0.935, and buying that precision costs recall against obfuscated input: across 35 attack families the model misses 617 of 2,070 obfuscated NCII prompts at 0.5 and 749 at 0.935. Which way to err depends on what we need to prioritize. Pick the threshold against the precision/recall tradeoff. The full sweep for dev here is in `eval/`, both on clean text and per attack family.
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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。