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Модель RuBERT была дообучена на задачу __sentiment classification__ для короткого __Russian__ корпуса. Задача представляет собой __multi-class classification__ со следующими метками:

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PolicyGuard-4B

Rakancorle1

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。

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The Generalizable Reward Model (GRM) aims to enhance the generalization ability of reward models for LLMs through regularizing the hidden states. The framework is shown above. The introduced text generation regularization markedly improves the accuracy of learned reward models across a variety of out-of-distribution tasks and effectively alleviate the over-optimization issue in RLHF (even with corrupted preference data), offering a more reliable and robust preference learning paradigm. This reward model is finetuned from gemma-2b-it using the weqweasdas/preference_dataset_mixture2_and_safe_pku dataset.

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This model is a fine-tuned version of Seznam/small-e-czech on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.2547 - Precision: 0.8713 - Recall: 0.8970 - F1: 0.8840 - Accuracy: 0.9557 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20

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