ajrayman/Consc_binary

1---2library_name: transformers3license: mit4base_model: microsoft/deberta-v3-base5tags:6- generated_from_trainer7metrics:8- accuracy9- precision10- recall11- f112model-index:13- name: Consc_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# Consc_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: 0.562125- Accuracy: 0.755926- Precision: 0.720427- Recall: 0.835428- F1: 0.773729- Auc: 0.842130 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  | 0.5627          | 0.6949   | 0.8095    | 0.5087 | 0.6248 | 0.8117 |62| No log        | 2.0   | 236  | 0.6050          | 0.6936   | 0.6307    | 0.9327 | 0.7525 | 0.8089 |63| No log        | 3.0   | 354  | 0.5123          | 0.7509   | 0.7014    | 0.8728 | 0.7778 | 0.8400 |64| No log        | 4.0   | 472  | 0.5966          | 0.7148   | 0.6525    | 0.9177 | 0.7627 | 0.8393 |65| 0.5227        | 5.0   | 590  | 0.5621          | 0.7559   | 0.7204    | 0.8354 | 0.7737 | 0.8421 |66 67 68### Framework versions69 70- Transformers 4.44.171- Pytorch 1.11.072- Datasets 2.12.073- Tokenizers 0.19.174