| license: mit | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - f1 | |
| - recall | |
| - precision | |
| base_model: ibm/ColD-Fusion | |
| model-index: | |
| - name: roberta_tec_gpu_v1 | |
| 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. --> | |
| # roberta_tec_gpu_v1 | |
| This model is a fine-tuned version of [ibm/ColD-Fusion](https://huggingface.co/ibm/ColD-Fusion) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2970 | |
| - F1: 0.8202 | |
| - Roc Auc: 0.8806 | |
| - Recall: 0.8561 | |
| - Precision: 0.7871 | |
| ## 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: 2 | |
| - eval_batch_size: 2 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 3.0 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Recall | Precision | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:------:|:---------:| | |
| | 0.4549 | 1.0 | 923 | 0.3128 | 0.7604 | 0.8277 | 0.7404 | 0.7815 | | |
| | 0.251 | 2.0 | 1846 | 0.2970 | 0.8202 | 0.8806 | 0.8561 | 0.7871 | | |
| | 0.1509 | 3.0 | 2769 | 0.3228 | 0.8146 | 0.8713 | 0.8246 | 0.8048 | | |
| ### Framework versions | |
| - Transformers 4.25.1 | |
| - Pytorch 1.13.1+cu117 | |
| - Datasets 2.8.0 | |
| - Tokenizers 0.13.2 | |