biodecision-v2-4b-GGUF
prithivMLmods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
Llama-Prompt-Guard-2-86M
project-free-llama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 ↗roberta-base-ca-v2-cased-ner
projecte-aina- Model description - Intended uses and limitations - How to use - Limitations and bias - Training - Training data - Training procedure - Evaluation - Variable and metrics - Evaluation results - Additional information - Author - Contact information - Copyright - Licensing information - Funding - Citing information - Disclaimer The **roberta-base-ca-v2-cased-ner** is a Named Entity Recognition (NER) model for the Catalan language fine-tuned from the roberta-base-ca-v2 model, a RoBERTa base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details). **roberta-base-ca-v2-cased-ner** model can be used to recognize Named Entities in the provided text. The model is limited by its training dataset and may not generalize well for all use cases.
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查看详情 ↗Prompt-Guard-86M_onnx
prompt-security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-NER-onnx
protectai> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. `bert-base-NER` is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). Specifically, this model is a `bert-base-cased` model that was fine-tuned on the English version of the standard `CoNLL-2003 Named Entity Recognition` dataset.
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查看详情 ↗codebert-base-Malicious_URLs-onnx
protectai> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. **This model is a conversion of DunnBC22/codebert-base-Malicious_URLs to ONNX** format. It's based on the CodeBERT architecture, tailored for the specific task of identifying URLs that may pose security threats. The model was converted to ONNX using the 🤗 Optimum library. **Modifications**: Details of any modifications or fine-tuning done to tailor the model for malicious URL detection.
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查看详情 ↗deberta-v3-base-injection-onnx
protectai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 ↗deberta-v3-base-prompt-injection
protectai> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. **There is a newer version of the model - protectai/deberta-v3-base-prompt-injection-v2.** This model is a fine-tuned version of microsoft/deberta-v3-base on multiple combined datasets of prompt injections and normal prompts.
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查看详情 ↗deberta-v3-small-prompt-injection-v2
protectai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 ↗distilroberta-base-rejection-v1
protectai> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. This model is a fine-tuned version of distilroberta-base on multiple combined datasets of rejections from different LLMs and normal responses from RLHF datasets. It aims to identify rejections in LLMs when the prompt doesn't pass content moderation, classifying inputs into two categories: `0` for normal outputs and `1` for rejection detected.
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查看详情 ↗distilroberta-bias-onnx
protectai> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. **This model is a conversion of valurank/distilroberta-bias to ONNX** format. It is designed to detect biases in text using the distilled version of the RoBERTa model. The model was converted to ONNX using the 🤗 Optimum library. **Base Model**: DistilRoBERTa, a distilled version of the RoBERTa model that is optimized for faster performance while maintaining similar accuracy.
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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 ↗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 ↗unbiased-toxic-roberta-onnx
protectai> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. **This model is a conversion of unitary/unbiased-toxic-roberta to ONNX** format using the 🤗 Optimum library. Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification.
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查看详情 ↗vishnun-codenlbert-sm-onnx
protectai> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. **This model is a conversion of vishnun/codenlbert-sm to ONNX** format using the 🤗 Optimum library.
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查看详情 ↗> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. **This model is a conversion of papluca/xlm-roberta-base-language-detection to ONNX** format using the 🤗 Optimum library. This model is a fine-tuned version of xlm-roberta-base on the Language Identification dataset.
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查看详情 ↗mdeberta-v3-base-prompt-injection
proventra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 ↗pubmedbert_bc5cdr
psgrghvuo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 ↗electra-small-discriminator-CoLA
pszemrajThis model is a fine-tuned version of google/electra-small-discriminator on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.4403 - Matthews Correlation: 0.5510 The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 512 - eval_batch_size: 16 - seed: 32754 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 8.0 - mixed_precision_training: Native AMP
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查看详情 ↗This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed]
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查看详情 ↗embedding
pyannote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-it-emotion
pysentimiento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-it-hate-speech
pysentimiento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。