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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: roberta_tec_gpu_v1 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# roberta_tec_gpu_v1 |
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This model is a fine-tuned version of [ibm/ColD-Fusion](https://huggingface.co/ibm/ColD-Fusion) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2970 |
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- F1: 0.8202 |
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- Roc Auc: 0.8806 |
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- Recall: 0.8561 |
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- Precision: 0.7871 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Recall | Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:------:|:---------:| |
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| 0.4549 | 1.0 | 923 | 0.3128 | 0.7604 | 0.8277 | 0.7404 | 0.7815 | |
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| 0.251 | 2.0 | 1846 | 0.2970 | 0.8202 | 0.8806 | 0.8561 | 0.7871 | |
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| 0.1509 | 3.0 | 2769 | 0.3228 | 0.8146 | 0.8713 | 0.8246 | 0.8048 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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