ppak10/defect-classification-scibert-prompt-05-epochs

1---2library_name: transformers3base_model: allenai/scibert_scivocab_uncased4tags:5- generated_from_trainer6metrics:7- accuracy8model-index:9- name: defect-classification-scibert-prompt-05-epochs10  results: []11---12 13<!-- This model card has been generated automatically according to the information the Trainer had access to. You14should probably proofread and complete it, then remove this comment. -->15 16# defect-classification-scibert-prompt-05-epochs17 18This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on an unknown dataset.19It achieves the following results on the evaluation set:20- Loss: 0.409421- Accuracy: 0.807822 23## Model description24 25More information needed26 27## Intended uses & limitations28 29More information needed30 31## Training and evaluation data32 33More information needed34 35## Training procedure36 37### Training hyperparameters38 39The following hyperparameters were used during training:40- learning_rate: 2e-0541- train_batch_size: 76842- eval_batch_size: 76843- seed: 4244- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments45- lr_scheduler_type: linear46- num_epochs: 547 48### Training results49 50| Training Loss | Epoch | Step   | Validation Loss | Accuracy |51|:-------------:|:-----:|:------:|:---------------:|:--------:|52| 0.3562        | 1.0   | 53084  | 0.4182          | 0.8043   |53| 0.3518        | 2.0   | 106168 | 0.3997          | 0.8114   |54| 0.3501        | 3.0   | 159252 | 0.4120          | 0.8073   |55| 0.3459        | 4.0   | 212336 | 0.4094          | 0.8067   |56| 0.3463        | 5.0   | 265420 | 0.4094          | 0.8078   |57 58 59### Framework versions60 61- Transformers 4.47.062- Pytorch 2.5.1+cu12463- Datasets 3.2.064- Tokenizers 0.21.065