FOMC-RoBERTa
gtfintechlab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
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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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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 ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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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 ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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