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nci-ner-v2

synapti

This model is a fine-tuned version of synapti/nci-ner-v2-staging on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4872 - Precision: 0.8799 - Recall: 0.9043 - F1: 0.8920 The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 0.05 - num_epochs: 3 - label_smoothing_factor: 0.05

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This model is a fine-tuned version of syssec-utd/py311-pylingual-v1-mlm on the syssec-utd/segmentation-py311-pylingual-v1-tokenized dataset. It achieves the following results on the evaluation set: - Loss: 0.0046 - Precision: 0.9958 - Recall: 0.9937 - F1: 0.9948 - Accuracy: 0.9983 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP

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This model is a fine-tuned version of syssec-utd/py312-pylingual-v1-mlm on the syssec-utd/segmentation-py312-pylingual-v1-tokenized dataset. It achieves the following results on the evaluation set: - Loss: 0.0053 - Precision: 0.9923 - Recall: 0.9935 - F1: 0.9929 - Accuracy: 0.9982 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP

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This model is a fine-tuned version of syssec-utd/py313-pylingual-v3-mlm on the syssec-utd/segmentation-py313-pylingual-v3-tokenized dataset. It achieves the following results on the evaluation set: - Loss: 0.0047 - Precision: 0.9945 - Recall: 0.9944 - F1: 0.9945 - Accuracy: 0.9983 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 28 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP

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ModernFinBERT

tabularisai

A fine-tuned financial sentiment analysis model based on **ModernBERT**, trained on synthetic and real financial data cleaned through an automated AI agentic pipeline. The model covers diverse financial domains including news, tweets, crypto, and macroeconomics, making it the most general-purpose financial sentiment classifier. Benchmark results show superior performance with up to 48% accuracy improvement over existing models across multiple financial datasets. For private API access or access to even more powerful financial models, contact us at **info@tabularis.ai**

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](https://discord.gg/sznxwdqBXj) --> [](https://discord.gg/sznxwdqBXj) - 2025/8: Major model update +1 new language: **Swahili**! Also, general improvements accross all languages. - 2025/7: We’ve just released ModernFinBERT, a model we’ve been working on for a while. It’s built on the ModernBERT architecture and trained on a mix of real and synthetic data, with LLM-based label correction applied to public datasets to fix human annotation errors. It’s performing well across a range of benchmarks — in some cases improving accuracy by up to 48% over existing models like FinBERT. You can check it out here on Hugging Face: 👉 https://huggingface.co/tabularisai/ModernFinBERT

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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。

基础资料已收录 · 缺失资料后续补充

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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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