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library_name: transformers
base_model: yusr9/radar-encoder-freeze
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: RADAR
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. -->
# RADAR
This model is a fine-tuned version of [yusr9/radar-encoder-freeze](https://huggingface.co/yusr9/radar-encoder-freeze) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1610
- Roc-auc: 0.99
- Brier: 0.967
- C@1: 0.964
- F1: 0.964
- F05u: 0.974
- Mean: 0.972
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.03
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Roc-auc | Brier | C@1 | F1 | F05u | Mean |
|:-------------:|:------:|:-----:|:---------------:|:-------:|:-----:|:-----:|:-----:|:-----:|:-----:|
| 0.2103 | 0.4153 | 500 | 0.1814 | 0.982 | 0.928 | 0.904 | 0.902 | 0.952 | 0.934 |
| 0.2102 | 0.8306 | 1000 | 0.1866 | 0.977 | 0.93 | 0.909 | 0.91 | 0.945 | 0.934 |
| 0.0821 | 1.2458 | 1500 | 0.2049 | 0.989 | 0.94 | 0.921 | 0.93 | 0.905 | 0.937 |
| 0.0476 | 1.6611 | 2000 | 0.0868 | 0.993 | 0.969 | 0.961 | 0.962 | 0.979 | 0.973 |
| 0.0551 | 2.0764 | 2500 | 0.0932 | 0.994 | 0.972 | 0.966 | 0.968 | 0.971 | 0.974 |
| 0.0306 | 2.4917 | 3000 | 0.1181 | 0.995 | 0.969 | 0.963 | 0.966 | 0.959 | 0.97 |
| 0.0295 | 2.9070 | 3500 | 0.0943 | 0.994 | 0.973 | 0.969 | 0.971 | 0.975 | 0.976 |
| 0.0345 | 3.3223 | 4000 | 0.1363 | 0.989 | 0.962 | 0.955 | 0.957 | 0.972 | 0.967 |
| 0.0555 | 3.7375 | 4500 | 0.1326 | 0.991 | 0.964 | 0.958 | 0.96 | 0.976 | 0.97 |
| 0.0493 | 4.1528 | 5000 | 0.1600 | 0.991 | 0.96 | 0.954 | 0.957 | 0.963 | 0.965 |
| 0.0113 | 4.5681 | 5500 | 0.1321 | 0.992 | 0.966 | 0.96 | 0.962 | 0.97 | 0.97 |
| 0.0074 | 4.9834 | 6000 | 0.1529 | 0.99 | 0.962 | 0.956 | 0.958 | 0.971 | 0.968 |
| 0.0515 | 5.3987 | 6500 | 0.1594 | 0.99 | 0.963 | 0.958 | 0.96 | 0.973 | 0.969 |
| 0.0059 | 5.8140 | 7000 | 0.1533 | 0.991 | 0.964 | 0.959 | 0.961 | 0.973 | 0.97 |
| 0.0174 | 6.2292 | 7500 | 0.1489 | 0.991 | 0.963 | 0.958 | 0.96 | 0.976 | 0.97 |
| 0.0230 | 6.6445 | 8000 | 0.1465 | 0.991 | 0.966 | 0.961 | 0.963 | 0.973 | 0.971 |
| 0.0128 | 7.0598 | 8500 | 0.1461 | 0.991 | 0.967 | 0.962 | 0.964 | 0.974 | 0.972 |
| 0.0408 | 7.4751 | 9000 | 0.1477 | 0.991 | 0.966 | 0.961 | 0.963 | 0.974 | 0.971 |
| 0.0057 | 7.8904 | 9500 | 0.1483 | 0.991 | 0.967 | 0.962 | 0.964 | 0.974 | 0.972 |
| 0.0262 | 8.3056 | 10000 | 0.1502 | 0.991 | 0.968 | 0.965 | 0.966 | 0.975 | 0.973 |
| 0.0198 | 8.7209 | 10500 | 0.1382 | 0.992 | 0.972 | 0.97 | 0.971 | 0.98 | 0.977 |
| 0.0111 | 9.1362 | 11000 | 0.1351 | 0.992 | 0.973 | 0.971 | 0.973 | 0.981 | 0.978 |
| 0.0016 | 9.5515 | 11500 | 0.1350 | 0.992 | 0.973 | 0.971 | 0.973 | 0.982 | 0.978 |
| 0.0029 | 9.9668 | 12000 | 0.1352 | 0.992 | 0.973 | 0.971 | 0.973 | 0.982 | 0.978 |
### Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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