Model overview
This is a SpanMarker model that can be used for multilingual Named Entity Recognition trained on the MultiNERD dataset. In particular, this SpanMarker model uses bert-base-multilingual-cased as the underlying encoder. See train.py for the training script. Is your data not (always) capitalized correctly? Then consider using this uncased variant of this model by @lxyuan for better performance: lxyuan/span-marker-bert-base-multilingual-uncased-multinerd. The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
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Estimate monthly base token costs
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Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
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Sources & traceability
许可证：cc-by-nc-sa-4.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。