Model overview
This is a fine-tuned Deberta model to detect human values in arguments. The model is part of the ensemble that was the best-performing system in the SemEval2023 task: Detecting Human Values in arguments It was trained and tested on a dataset of 9324 annotated arguments. The whole ensemble system achieved a F1-Score of 0.56 in the competiton. This model achieves a F1-Score of 0.55. Code for retraining the ensemble is accessible in this repo This model is built on custom code. So the inference api cannot be used directly. To use the model please follow the steps below... To make a prediction and map the the outputs to the correct labels. During the competiton a threshold of 0.25 was used to binarize the output.
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
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
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Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
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?
Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.
Are these real-time billing prices?
These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.
Sources & traceability
许可证：openrail++。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。