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---
license: mit
base_model: haryoaw/scenario-MDBT-TCR_data-cl-massive_all_1_1
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
- f1
model-index:
- name: scenario-KD-PR-MSV-D2_data-cl-massive_all_1_166
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. -->
# scenario-KD-PR-MSV-D2_data-cl-massive_all_1_166
This model is a fine-tuned version of [haryoaw/scenario-MDBT-TCR_data-cl-massive_all_1_1](https://huggingface.co/haryoaw/scenario-MDBT-TCR_data-cl-massive_all_1_1) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5375
- Accuracy: 0.6228
- F1: 0.5882
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 66
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
| 1.3672 | 0.56 | 5000 | 2.2863 | 0.6347 | 0.5695 |
| 1.1254 | 1.11 | 10000 | 2.2766 | 0.6389 | 0.5787 |
| 1.096 | 1.67 | 15000 | 2.3272 | 0.6320 | 0.5950 |
| 1.0005 | 2.22 | 20000 | 2.3817 | 0.6288 | 0.5832 |
| 1.0016 | 2.78 | 25000 | 2.3657 | 0.6298 | 0.5844 |
| 0.9507 | 3.33 | 30000 | 2.3644 | 0.6336 | 0.5859 |
| 0.9575 | 3.89 | 35000 | 2.3989 | 0.6260 | 0.5880 |
| 0.9037 | 4.45 | 40000 | 2.4691 | 0.6229 | 0.5865 |
| 0.9175 | 5.0 | 45000 | 2.4481 | 0.6209 | 0.5752 |
| 0.8991 | 5.56 | 50000 | 2.5361 | 0.6132 | 0.5801 |
| 0.8665 | 6.11 | 55000 | 2.5003 | 0.6167 | 0.5735 |
| 0.8734 | 6.67 | 60000 | 2.4807 | 0.6249 | 0.5832 |
| 0.8588 | 7.23 | 65000 | 2.5712 | 0.6115 | 0.5672 |
| 0.8629 | 7.78 | 70000 | 2.5958 | 0.6076 | 0.5746 |
| 0.8508 | 8.34 | 75000 | 2.5262 | 0.6229 | 0.5849 |
| 0.8543 | 8.89 | 80000 | 2.5397 | 0.6171 | 0.5799 |
| 0.8426 | 9.45 | 85000 | 2.5143 | 0.6119 | 0.5634 |
| 0.8377 | 10.0 | 90000 | 2.5661 | 0.6131 | 0.5808 |
| 0.8317 | 10.56 | 95000 | 2.5662 | 0.6168 | 0.5770 |
| 0.8231 | 11.12 | 100000 | 2.5272 | 0.6207 | 0.5775 |
| 0.8231 | 11.67 | 105000 | 2.5792 | 0.6047 | 0.5625 |
| 0.8198 | 12.23 | 110000 | 2.5869 | 0.6144 | 0.5783 |
| 0.8219 | 12.78 | 115000 | 2.5868 | 0.6126 | 0.5745 |
| 0.8131 | 13.34 | 120000 | 2.6226 | 0.6043 | 0.5658 |
| 0.8113 | 13.9 | 125000 | 2.5777 | 0.6174 | 0.5807 |
| 0.8122 | 14.45 | 130000 | 2.6451 | 0.6022 | 0.5787 |
| 0.8124 | 15.01 | 135000 | 2.5426 | 0.6215 | 0.5847 |
| 0.8106 | 15.56 | 140000 | 2.6562 | 0.6031 | 0.5774 |
| 0.8046 | 16.12 | 145000 | 2.6410 | 0.6059 | 0.5703 |
| 0.8031 | 16.67 | 150000 | 2.6155 | 0.6088 | 0.5794 |
| 0.7949 | 17.23 | 155000 | 2.6978 | 0.5997 | 0.5698 |
| 0.799 | 17.79 | 160000 | 2.6272 | 0.6102 | 0.5783 |
| 0.7964 | 18.34 | 165000 | 2.5934 | 0.6161 | 0.5765 |
| 0.7943 | 18.9 | 170000 | 2.5863 | 0.6142 | 0.5722 |
| 0.793 | 19.45 | 175000 | 2.5353 | 0.6224 | 0.5762 |
| 0.7919 | 20.01 | 180000 | 2.6723 | 0.6057 | 0.5759 |
| 0.7893 | 20.56 | 185000 | 2.6377 | 0.6098 | 0.5820 |
| 0.7864 | 21.12 | 190000 | 2.6707 | 0.6057 | 0.5824 |
| 0.79 | 21.68 | 195000 | 2.7768 | 0.5904 | 0.5802 |
| 0.7871 | 22.23 | 200000 | 2.6895 | 0.6001 | 0.5734 |
| 0.786 | 22.79 | 205000 | 2.6505 | 0.6063 | 0.5827 |
| 0.7862 | 23.34 | 210000 | 2.5607 | 0.6200 | 0.5876 |
| 0.7863 | 23.9 | 215000 | 2.6414 | 0.6082 | 0.5828 |
| 0.7839 | 24.46 | 220000 | 2.5978 | 0.6125 | 0.5883 |
| 0.7828 | 25.01 | 225000 | 2.6076 | 0.6125 | 0.5804 |
| 0.7838 | 25.57 | 230000 | 2.6193 | 0.6097 | 0.5778 |
| 0.7825 | 26.12 | 235000 | 2.5599 | 0.6184 | 0.5860 |
| 0.7832 | 26.68 | 240000 | 2.5363 | 0.6227 | 0.5857 |
| 0.7788 | 27.23 | 245000 | 2.5842 | 0.6199 | 0.5930 |
| 0.7768 | 27.79 | 250000 | 2.5907 | 0.6170 | 0.5889 |
| 0.7802 | 28.35 | 255000 | 2.5625 | 0.6196 | 0.5895 |
| 0.7808 | 28.9 | 260000 | 2.5512 | 0.6220 | 0.5903 |
| 0.7776 | 29.46 | 265000 | 2.5375 | 0.6228 | 0.5882 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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