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Models by this creator
The roberta-base-cold model is a text classification model trained on a large amount of data. It is based on the RoBERTa architecture, which is a variant of the BERT model. The model is useful for tasks such as sentiment analysis, topic classification, and text categorization. It is designed to classify text into different categories based on the content of the text. The model has been trained to understand the patterns and relationships in the text, allowing it to accurately classify new, unseen text.
EVA Model Description EVA is the largest open-source Chinese dialogue model with up to 2.8B parameters. The 1.0 version model is pre-trained on WudaoCorpus-Dialog, and the 2.0 version is pre-trained on a carefully cleaned version of WudaoCorpus-Dialog which yields better performance than the 1.0 version. Paper link of EVA1.0. Paper link of EVA2.0. Model Configuration How to use Please refer to our GitHub repository. Performance We use the KdConv dataset to finetune and evaluate the model. Under the default hyperparameters in the scripts, we get the following results on the test set: We also use beam search to run the interactive inference of EVA2.0: NOET: Since different hardware may have different implementations of random functions, even if you use the same random seed as ours, you may not be able to reproduce this case. But the overall performance will not differ much. Disclaimer The pre-trained models aim to facilitate the research for conversation generation. The model provided in this repository is trained on a large dataset collected from various sources. Although a rigorous cleaning and filtering process has been carried out to the data and the model output, there is no guarantee that all the inappropriate contents have been completely banned. All the contents generated by the model do not represent the authors' opinions. The decoding script provided in this repository is only for research purposes. We are not responsible for any content generated using our model. Citation