ajrayman/AuthorityRespect_binary

1---2library_name: transformers3license: mit4base_model: microsoft/deberta-v3-base5tags:6- generated_from_trainer7metrics:8- accuracy9- precision10- recall11- f112model-index:13- name: AuthorityRespect_binary14  results: []15---16 17<!-- This model card has been generated automatically according to the information the Trainer had access to. You18should probably proofread and complete it, then remove this comment. -->19 20# AuthorityRespect_binary21 22This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.23It achieves the following results on the evaluation set:24- Loss: 0.618425- Accuracy: 0.646326- Precision: 0.673027- Recall: 0.569128- F1: 0.616729- Auc: 0.726830 31## Model description32 33More information needed34 35## Intended uses & limitations36 37More information needed38 39## Training and evaluation data40 41More information needed42 43## Training procedure44 45### Training hyperparameters46 47The following hyperparameters were used during training:48- learning_rate: 2e-0549- train_batch_size: 3250- eval_batch_size: 3251- seed: 123452- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0853- lr_scheduler_type: linear54- lr_scheduler_warmup_ratio: 0.0655- num_epochs: 856 57### Training results58 59| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Auc    |60|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|61| No log        | 1.0   | 110  | 0.6295          | 0.6582   | 0.6476    | 0.6941 | 0.6701 | 0.7028 |62| No log        | 2.0   | 220  | 0.6181          | 0.6622   | 0.6572    | 0.6782 | 0.6675 | 0.7186 |63| No log        | 3.0   | 330  | 0.6184          | 0.6463   | 0.6730    | 0.5691 | 0.6167 | 0.7268 |64 65 66### Framework versions67 68- Transformers 4.44.169- Pytorch 1.11.070- Datasets 2.12.071- Tokenizers 0.19.172