inappropriate_text_classifier
michelleli99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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探索 AI 模型 2428
NSFW_text_classifier
michelleli99DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on Reddit posts with the purpose of classifying not safe for work (NSFW) content, specifically text that is considered inappropriate and unprofessional. The model predicts 2 classes, which are NSFW or safe for work (SFW). It was fine-tuned on 14317 Reddit posts pulled from the (Reddit API) [https://praw.readthedocs.io/en/stable/].
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查看详情 ↗BioLinkBERT-base
michiyasunagaBioLinkBERT-base model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository. This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as BLURB and MedQA-USMLE. LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document.
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查看详情 ↗BioLinkBERT-large
michiyasunagaBioLinkBERT-large model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository. This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as BLURB and MedQA-USMLE. LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document.
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查看详情 ↗LinkBERT-base
michiyasunaga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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michiyasunaga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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deberta-base-mnli
MicrosoftPlease check the official repository for more details and updates.
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查看详情 ↗deberta-large-mnli
MicrosoftPlease check the official repository for more details and updates.
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查看详情 ↗deberta-v2-xlarge-mnli
MicrosoftDeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data. This the DeBERTa V2 xlarge model fine-tuned with MNLI task, 24 layers, 1536 hidden size. Total parameters 900M.
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查看详情 ↗deberta-v2-xxlarge-mnli
Microsoft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-xlarge-mnli
MicrosoftPlease check the official repository for more details and updates.
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查看详情 ↗DialogRPT-depth
Microsoft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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Microsoft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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Microsoft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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Microsoft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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Microsoft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 model was introduced in the paper **LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression** (Pan et al, 2024). It is a XLM-RoBERTa (large-sized model) finetuned to perform token classification for task agnostic prompt compression. The probability $p_{preserve}$ of each token $x_i$ is used as the metric for compression. This model is trained on the extractive text compression dataset constructed with the methodology proposed in the **LLMLingua-2**, using training examples from MeetingBank (Hu et al, 2023) as the seed data. You can evaluate the model on downstream tasks such as question answering (QA) and summarization over compressed meeting transcripts using this dataset. For more details, please check the home page of LLMLingua-2 and LLMLingua Series.
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查看详情 ↗MiniLM-L12-H384-uncased
MicrosoftMiniLM is a distilled model from the paper "MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers". Please find the information about preprocessing, training and full details of the MiniLM in the original MiniLM repository. Please note: This checkpoint can be an inplace substitution for BERT and it needs to be fine-tuned before use!
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查看详情 ↗Multilingual-MiniLM-L12-H384
MicrosoftMiniLM is a distilled model from the paper "MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers". Please find the information about preprocessing, training and full details of the MiniLM in the original MiniLM repository. Please note: This checkpoint uses `BertModel` with `XLMRobertaTokenizer` so `AutoTokenizer` won't work with this checkpoint!
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查看详情 ↗tapex-base-finetuned-tabfact
Microsoft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 ↗wavlm-large
MicrosoftThe large model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. - 60,000 hours of Libri-Light - 10,000 hours of GigaSpeech - 24,000 hours of VoxPopuli
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查看详情 ↗xdoc-base-funsd
Microsoft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 ↗xtremedistil-l12-h384-uncased
Microsoft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 ↗xtremedistil-l6-h256-uncased
MicrosoftXtremeDistilTransformers is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages as outlined in the paper XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation. We leverage task transfer combined with multi-task distillation techniques from the papers XtremeDistil: Multi-stage Distillation for Massive Multilingual Models and MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers with the following Github code. This l6-h384 checkpoint with **6** layers, **384** hidden size, **12** attention heads corresponds to **22 million** parameters with **5.3x** speedup over BERT-base.
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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。