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---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
- recall
- precision
base_model: ibm/ColD-Fusion
model-index:
- name: cold_reman_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. -->
# cold_reman_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: 0.4520
- F1: 0.6592
- Roc Auc: 0.7559
- Recall: 0.6197
- Precision: 0.704
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:------:|:---------:|
| No log | 1.0 | 452 | 0.4556 | 0.6 | 0.7160 | 0.5282 | 0.6944 |
| 0.4832 | 2.0 | 904 | 0.4520 | 0.6592 | 0.7559 | 0.6197 | 0.704 |
| 0.3505 | 3.0 | 1356 | 0.4658 | 0.6543 | 0.7530 | 0.6197 | 0.6929 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
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