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for 5 epochs with a batch size of 32, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.850431447746884, as measured by the eval set accuracy, found after 1 epoch.
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This is an instance of bert-base-multilingual-cased that was fine-tuned on binary toxicity classification task based on our updated (2025) dataset textdetox/multilingual_toxicity_dataset. The model is prepared for TextDetox 2025 Shared Task evaluation.
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This is an instance of cardiffnlp/twitter-xlm-roberta-large-2022 that was fine-tuned on binary toxicity classification task based on our updated (2025) dataset textdetox/multilingual_toxicity_dataset.
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This is an instance of xlm-roberta-large that was fine-tuned on binary toxicity classification task based on our updated (2025) dataset textdetox/multilingual_toxicity_dataset.
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General Text Embeddings (GTE) model. Towards General Text Embeddings with Multi-stage Contrastive Learning The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the MTEB leaderboard.
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A Vision Transformer (ViT) image feature model. Pretrained on LVD-142M with self-supervised DINOv2 method. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 86.6 - GMACs: 151.7 - Activations (M): 397.6 - Image size: 518 x 518 - **Papers:** - DINOv2: Learning Robust Visual Features without Supervision: https://arxiv.org/abs/2304.07193 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Original:** https://github.com/facebookresearch/dinov2 - **Pretrain Dataset:** LVD-142M Explore the dataset and runtime metrics of this model in timm model results.
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A Vision Transformer (ViT) image feature model. Pretrained on LVD-142M with self-supervised DINOv2 method. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 22.1 - GMACs: 46.8 - Activations (M): 198.8 - Image size: 518 x 518 - **Papers:** - DINOv2: Learning Robust Visual Features without Supervision: https://arxiv.org/abs/2304.07193 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Original:** https://github.com/facebookresearch/dinov2 - **Pretrain Dataset:** LVD-142M Explore the dataset and runtime metrics of this model in timm model results.
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ONNX export of Davlan/xlm-roberta-base-ner-hrl for use with ONNX Runtime, Transformers.js, and bridge-anonymization.
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This model is a fine-tuned version of microsoft/deberta-v3-large on the tner/ontonotes5 dataset. Model fine-tuning is done via T-NER's hyper-parameter search (see the repository for more detail). It achieves the following results on the test set: - F1 (micro): 0.9069623608411381 - Precision (micro): 0.902100360312857 - Recall (micro): 0.9118770542773386 - F1 (macro): 0.834586960779896 - Precision (macro): 0.8237351069457466 - Recall (macro): 0.8475169311172334 The per-entity breakdown of the F1 score on the test set are below: - cardinal_number: 0.853475935828877 - date: 0.8815545959284392 - event: 0.8030303030303031 - facility: 0.7896678966789669 - geopolitical_area: 0.9650033867690223 - group: 0.9337209302325581 - language: 0.8372093023255814 - law: 0.6756756756756757 - location: 0.7624020887728459 - money: 0.8818897637795275 - ordinal_number: 0.8635235732009926 - organization: 0.914952751528627 - percent: 0.9 - person: 0.9609866599546942 - product: 0.7901234567901234 - quantity: 0.8161434977578474 - time: 0.674364896073903 - work_of_art: 0.7188405797101449 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.9019409960743083, 0.911751130722053] - 95%: [0.9010822890967028, 0.9125611412371442] - F1 (macro): - 90%: [0.9019409960743083, 0.911751130722053] - 95%: [0.9010822890967028, 0.9125611412371442]
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This model is a fine-tuned version of roberta-large on the tner/tweetner7 dataset (`train_all` split). Model fine-tuning is done via T-NER's hyper-parameter search (see the repository for more detail). It achieves the following results on the test set of 2021: - F1 (micro): 0.6574551220340903 - Precision (micro): 0.644212629008989 - Recall (micro): 0.6712534690101758 - F1 (macro): 0.6124665667529737 - Precision (macro): 0.6005167968535563 - Recall (macro): 0.625251837701222 The per-entity breakdown of the F1 score on the test set are below: - corporation: 0.5392156862745098 - creative_work: 0.4760582928521859 - event: 0.4673321234119782 - group: 0.6139798488664987 - location: 0.6707399864222675 - person: 0.8293212669683258 - product: 0.6906187624750498 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.6484148010152769, 0.6672289519134409] - 95%: [0.6470100684797441, 0.6689850350992637] - F1 (macro): - 90%: [0.6484148010152769, 0.6672289519134409] - 95%: [0.6470100684797441, 0.6689850350992637]
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可以调整厂商、分类或输入输出条件；新模型正持续收录。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
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。
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。
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。
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。
