mMiniLM-L6-v2-mmarco-v2
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 2428
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查看详情 ↗**⚠️ Disclaimer:** The huggingface models currently give different results to the detoxify library (see issue here). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. Built by Laura Hanu at Unitary, where we are working to stop harmful content online by interpreting visual content in context.
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查看详情 ↗toxic-bert
unitary**⚠️ Disclaimer:** The huggingface models currently give different results to the detoxify library (see issue here). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. Built by Laura Hanu at Unitary, where we are working to stop harmful content online by interpreting visual content in context.
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查看详情 ↗unbiased-toxic-roberta
unitary**⚠️ Disclaimer:** The huggingface models currently give different results to the detoxify library (see issue here). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. Built by Laura Hanu at Unitary, where we are working to stop harmful content online by interpreting visual content in context.
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查看详情 ↗gemma-2b-it-bnb-4bit
unsloth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 ↗1
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查看详情 ↗repeat
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查看详情 ↗gliner_base
urchade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 ↗gliner_large-v2
urchadeGLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. This version has been trained on the **NuNER** dataset (commercially permissive). * Paper: https://arxiv.org/abs/2311.08526 * Repository: https://github.com/urchade/GLiNER
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查看详情 ↗gliner_large-v2.1
urchadeGLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.
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查看详情 ↗gliner_medium-v2.1
urchadeGLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. * Paper: https://arxiv.org/abs/2311.08526 * Repository: https://github.com/urchade/GLiNER Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.
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查看详情 ↗gliner_multi
urchadeGLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. This version has been trained on the **Pile-NER** dataset (Research purpose). Commercially permission versions are available (**urchade/gliner_smallv2**, **urchade/gliner_mediumv2**, **urchade/gliner_largev2**) Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.
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查看详情 ↗gliner_multi_pii-v1
urchadeGLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. This model has been trained by fine-tuning `urchade/gliner_multi-v2.1` on the `urchade/synthetic-pii-ner-mistral-v1` dataset. This model is capable of recognizing various types of *personally identifiable information* (PII), including but not limited to these entity types: `person`, `organization`, `phone number`, `address`, `passport number`, `email`, `credit card number`, `social security number`, `health insurance id number`, `date of birth`, `mobile phone number`, `bank account number`, `medication`, `cpf`, `driver's license number`, `tax identification number`, `medical condition`, `identity card number`, `national id number`, `ip address`, `email address`, `iban`, `credit card expiration date`, `username`, `health insurance number`, `registration number`, `student id number`, `insurance number`, `flight number`, `landline phone number`, `blood type`, `cvv`, `reservation number`, `digital signature`, `social media handle`, `license plate number`, `cnpj`, `postal code`, `passport_number`, `serial number`, `vehicle registration number`, `credit card brand`, `fax number`, `visa number`, `insurance company`, `identity document number`, `transaction number`, `national health insurance number`, `cvc`, `birth certificate number`, `train ticket number`, `passport expiration date`, and `social_security_number`.
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查看详情 ↗gliner_multi-v2.1
urchadeGLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. * Paper: https://arxiv.org/abs/2311.08526 * Repository: https://github.com/urchade/GLiNER Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.
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查看详情 ↗gliner_small-v1
urchade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 ↗gliner_small-v2.1
urchadeGLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. * Paper: https://arxiv.org/abs/2311.08526 * Repository: https://github.com/urchade/GLiNER Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.
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查看详情 ↗distilroberta-bias
valurankThis model is based on distilroberta-base pretrained weights, with a classification head fine-tuned to classify text into 2 categories (neutral, biased). The dataset used to fine-tune the model is wikirev-bias, extracted from English wikipedia revisions, see https://github.com/rpryzant/neutralizing-bias for details on the WNC wiki edits corpus.
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查看详情 ↗distilroberta-clickbait
valurank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 ↗FinBERT-India-v1
Vansh180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 ↗NADI2024-baseline
VARabi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 ↗Sentence-ALDi
VARabi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 ↗👉 **Launch Interactive Demo** - No setup required, runs in your browser. Paste a source text and an LLM response to get a verdict, a 0-1 consistency score, and per-sentence scores. 💡 **Quick test**: Try "The capital of France is Berlin" as the source and "The capital of France is Paris" as the response to see HHEM detect this factual but hallucinated case. HHEM-2.1-Open is the latest open source version of Vectara's HHEM series models for detecting hallucinations in LLMs. These are particularly useful in the context of building retrieval-augmented-generation (RAG) applications or Agentic workflows, where a set of facts is summarized by an LLM, and HHEM can be used to measure the extent to which this summary is factually consistent with the facts.
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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。