| | --- |
| | library_name: transformers |
| | base_model: allenai/scibert_scivocab_uncased |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: Final_ManufacturedObjects_STL_model |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # Final_ManufacturedObjects_STL_model |
| | |
| | This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1199 |
| | - Precision: 0.9873 |
| | - Recall: 0.9852 |
| | - F1: 0.9863 |
| | - Accuracy: 0.9825 |
| | |
| | ## Model description |
| | |
| | More information needed |
| | |
| | ## Intended uses & limitations |
| | |
| | More information needed |
| | |
| | ## Training and evaluation data |
| | |
| | More information needed |
| | |
| | ## Training procedure |
| | |
| | ### Training hyperparameters |
| | |
| | The following hyperparameters were used during training: |
| | - learning_rate: 2e-05 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: linear |
| | - num_epochs: 10 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 0.1069 | 1.0 | 523 | 0.0714 | 0.9823 | 0.9791 | 0.9807 | 0.9758 | |
| | | 0.0411 | 2.0 | 1046 | 0.0689 | 0.9873 | 0.9827 | 0.9850 | 0.9810 | |
| | | 0.0221 | 3.0 | 1569 | 0.0726 | 0.9864 | 0.9845 | 0.9854 | 0.9812 | |
| | | 0.0132 | 4.0 | 2092 | 0.0867 | 0.9870 | 0.9856 | 0.9863 | 0.9820 | |
| | | 0.0081 | 5.0 | 2615 | 0.0973 | 0.9868 | 0.9853 | 0.9861 | 0.9820 | |
| | | 0.0042 | 6.0 | 3138 | 0.1079 | 0.9875 | 0.9852 | 0.9863 | 0.9823 | |
| | | 0.0031 | 7.0 | 3661 | 0.1179 | 0.9881 | 0.9856 | 0.9868 | 0.9825 | |
| | | 0.0016 | 8.0 | 4184 | 0.1146 | 0.9881 | 0.9859 | 0.9870 | 0.9833 | |
| | | 0.0013 | 9.0 | 4707 | 0.1190 | 0.9879 | 0.9856 | 0.9867 | 0.9831 | |
| | | 0.0009 | 10.0 | 5230 | 0.1199 | 0.9873 | 0.9852 | 0.9863 | 0.9825 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.47.1 |
| | - Pytorch 2.5.1+cu124 |
| | - Datasets 3.2.0 |
| | - Tokenizers 0.21.0 |
| | |
