---
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
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# 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