ajrayman/Intellect_binary

1---2library_name: transformers3license: mit4base_model: microsoft/deberta-v3-base5tags:6- generated_from_trainer7metrics:8- accuracy9- precision10- recall11- f112model-index:13- name: Intellect_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# Intellect_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: 0.659625- Accuracy: 0.648826- Precision: 0.645527- Recall: 0.658428- F1: 0.651929- Auc: 0.693230 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   | 118  | 0.6559          | 0.6164   | 0.5766    | 0.8728 | 0.6944 | 0.7155 |62| No log        | 2.0   | 236  | 0.6098          | 0.6874   | 0.6812    | 0.7032 | 0.6920 | 0.7340 |63| No log        | 3.0   | 354  | 0.6596          | 0.6488   | 0.6455    | 0.6584 | 0.6519 | 0.6932 |64 65 66### Framework versions67 68- Transformers 4.44.169- Pytorch 1.11.070- Datasets 2.12.071- Tokenizers 0.19.172