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This model uses SOTA models to detect AI generated content. Label_0 represents Fake Label_1 represents Real View our website: AI Content Detector

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Clef-Flash-GGUF

Pitaya1219

Low-bit quantizations (Q4_K_M, IQ3_XXS, IQ2_M, IQ2_XS) of Cloudflare/clef-flash in the native `clef` GGUF architecture. Serve with `llama-server` b11415 or later (`/v1/systemone`, llama.cpp#29831). Run through `llama-server` b11415 (`/v1/systemone`, Vulkan, AMD Radeon 8060S), one request at a time, with the same 600 items for every model: 200 each from SST-2 (validation), AG News (test) and BoolQ (validation), sampled with a fixed seed. Agreement and KL are against `Clef-Flash-Q8_0.gguf` from ggml-org, not against bf16. Livesport's per-tensor layout applied step by step to IQ2_M (all with the head at Q8_0), same 600 items, against the same Q8_0 baseline.

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This is a Spacy multilingual (Catalan & Spanish) anonimization model, for use with BSC's AnonymizationPipeline at: The anonymization pipeline is a library for performing sensitive data identification and ultimately anonymization of the detected data in Spanish and Catalan user generated plain text. The model can detect the following entities: `EMAIL`, `FINANCIAL`, `ID`, `LOC`, `MISC`, `ORG`, `PER`, `TELEPHONE`, `VEHICLE`, `ZIP`

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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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foxmind

pooria

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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The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository. These BERT variants were introduced in the paper Well-Read Students Learn Better: On the Importance of Pre-training Compact Models. These models are trained on MNLI.

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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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nf-pilot

Primeomicx

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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QuRater-1.3B

princeton-nlp

The model is a sequence classification model that predicts quality ratings across 4 critera as outputs. The quality ratings (which are unnormalized) can be accessed via the 4 logits in the output of the model. They are in the following order: * Logit 0: Writing Style * Logit 1: Required Expertise * Logit 2: Facts and Trivia * Logit 3: Educational Value **Note:** The model was only fine-tuned on sequences of up to 512 tokens, and should not be used for longer documents. Instead, compute the quality ratings for windows of up to 512 token and average them weighted by the window length. In the paper, we document various types of bias that are present in the quality ratings from the QuRater model (biases related to domains, topics, social roles, regions and languages - see Section 6 of the paper). Hence, be aware that data selection with QuRating could have unintended and harmful effects on the language model that is being trained. We strongly recommend a comprehensive evaluation of the language model for these and other types of bias, particularly before real-world deployment. We hope that releasing the data/models can facilitate future research aimed at uncovering and mitigating such biases. Note that the quality ratings do not measure the social or literary value of a text and should *not* be used for textual or demographic studies.

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BERTAIDetector

pritamdeb68

This model is a fine-tuned BERT designed to classify text as either AI-generated or human-written. The model was trained on data from the Kaggle LLM Detect competition using variable-length text inputs ranging from 5 to 100 words. The fine-tuned model achieves high accuracy in identifying the source of the text, making it a valuable tool for detecting AI-generated content. - **Developed by:** Pritam - **Language(s) (NLP):** English - **License:** Apache 2.0 - **Finetuned from model:** BERT (base-uncased) - **Repository:** Hugging Face Model Card - **Demo:** Streamlit Interface

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This model is a fine-tuned version of PubMedBERT on the MNLI dataset first and then on the MedNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.9501 - Accuracy: 0.8667 The model can be used for NLI tasks related to biomedical data and even be adapted to fact-checking tasks. It can be used from the Huggingface pipeline method as follows: The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0

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COST EXPLORER

好模型，也要算好成本。

费用分析

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

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

选型与常见问题
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。

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