Illustration of our proposed InfoDCL framework. We exploit distant/surrogate labels (i.e., emojis) to supervise two contrastive losses, corpus-aware contrastive loss (CCL) and Light label-aware contrastive loss (LCL-LiT). Sequence representations from our model should keep the cluster of each class distinguishable and preserve semantic relationships between classes.
InfoDCL-emoji huggingface.co is an AI model on huggingface.co that provides InfoDCL-emoji's model effect (), which can be used instantly with this UBC-NLP InfoDCL-emoji model. huggingface.co supports a free trial of the InfoDCL-emoji model, and also provides paid use of the InfoDCL-emoji. Support call InfoDCL-emoji model through api, including Node.js, Python, http.
InfoDCL-emoji huggingface.co is an online trial and call api platform, which integrates InfoDCL-emoji's modeling effects, including api services, and provides a free online trial of InfoDCL-emoji, you can try InfoDCL-emoji online for free by clicking the link below.
UBC-NLP InfoDCL-emoji online free url in huggingface.co:
InfoDCL-emoji is an open source model from GitHub that offers a free installation service, and any user can find InfoDCL-emoji on GitHub to install. At the same time, huggingface.co provides the effect of InfoDCL-emoji install, users can directly use InfoDCL-emoji installed effect in huggingface.co for debugging and trial. It also supports api for free installation.