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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-PO-MSV-CL-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-PO-MSV-CL-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: 6.0186
- Accuracy: 0.6461
- F1: 0.6134
## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
| 2.2524 | 0.56 | 5000 | 5.6187 | 0.6299 | 0.5728 |
| 1.3325 | 1.11 | 10000 | 5.4671 | 0.6450 | 0.5924 |
| 1.2156 | 1.67 | 15000 | 6.0747 | 0.6250 | 0.5912 |
| 0.8855 | 2.22 | 20000 | 5.8471 | 0.6355 | 0.5857 |
| 0.8518 | 2.78 | 25000 | 6.2545 | 0.6303 | 0.5845 |
| 0.6853 | 3.33 | 30000 | 6.0057 | 0.6408 | 0.6017 |
| 0.6658 | 3.89 | 35000 | 6.0161 | 0.6423 | 0.6002 |
| 0.5544 | 4.45 | 40000 | 6.0854 | 0.6392 | 0.6006 |
| 0.5357 | 5.0 | 45000 | 6.2732 | 0.6283 | 0.5888 |
| 0.4924 | 5.56 | 50000 | 6.4624 | 0.6277 | 0.5952 |
| 0.4369 | 6.11 | 55000 | 6.2119 | 0.6354 | 0.5944 |
| 0.4276 | 6.67 | 60000 | 6.2395 | 0.6425 | 0.6006 |
| 0.3974 | 7.23 | 65000 | 6.6542 | 0.6264 | 0.5893 |
| 0.404 | 7.78 | 70000 | 6.4174 | 0.6295 | 0.5975 |
| 0.3763 | 8.34 | 75000 | 6.1405 | 0.6426 | 0.6025 |
| 0.3719 | 8.89 | 80000 | 6.4745 | 0.6346 | 0.6024 |
| 0.3428 | 9.45 | 85000 | 5.9964 | 0.6389 | 0.6030 |
| 0.3288 | 10.0 | 90000 | 6.3213 | 0.6335 | 0.5988 |
| 0.3192 | 10.56 | 95000 | 6.4269 | 0.6321 | 0.5937 |
| 0.2934 | 11.12 | 100000 | 6.3224 | 0.6392 | 0.6039 |
| 0.3054 | 11.67 | 105000 | 6.4531 | 0.6326 | 0.5989 |
| 0.2841 | 12.23 | 110000 | 6.2824 | 0.6360 | 0.6075 |
| 0.2915 | 12.78 | 115000 | 6.1928 | 0.6391 | 0.6039 |
| 0.274 | 13.34 | 120000 | 6.1931 | 0.6401 | 0.6030 |
| 0.2776 | 13.9 | 125000 | 6.2524 | 0.6384 | 0.6045 |
| 0.2724 | 14.45 | 130000 | 5.9260 | 0.6456 | 0.6090 |
| 0.2602 | 15.01 | 135000 | 6.3508 | 0.6347 | 0.6052 |
| 0.2627 | 15.56 | 140000 | 6.1761 | 0.6421 | 0.6074 |
| 0.2496 | 16.12 | 145000 | 6.1398 | 0.6391 | 0.6111 |
| 0.253 | 16.67 | 150000 | 6.2431 | 0.6328 | 0.6014 |
| 0.2451 | 17.23 | 155000 | 6.1746 | 0.6378 | 0.6048 |
| 0.2369 | 17.79 | 160000 | 6.0915 | 0.6435 | 0.6103 |
| 0.2332 | 18.34 | 165000 | 6.2138 | 0.6376 | 0.6071 |
| 0.2325 | 18.9 | 170000 | 6.1176 | 0.6433 | 0.6073 |
| 0.2239 | 19.45 | 175000 | 5.9650 | 0.6419 | 0.6068 |
| 0.2229 | 20.01 | 180000 | 6.2025 | 0.6395 | 0.6072 |
| 0.2241 | 20.56 | 185000 | 6.0510 | 0.6418 | 0.6088 |
| 0.212 | 21.12 | 190000 | 5.9952 | 0.6438 | 0.6100 |
| 0.218 | 21.68 | 195000 | 6.2810 | 0.6376 | 0.6073 |
| 0.212 | 22.23 | 200000 | 5.9274 | 0.6454 | 0.6076 |
| 0.2091 | 22.79 | 205000 | 6.1958 | 0.6367 | 0.6071 |
| 0.2091 | 23.34 | 210000 | 5.9633 | 0.6463 | 0.6153 |
| 0.2065 | 23.9 | 215000 | 6.0132 | 0.6458 | 0.6116 |
| 0.2048 | 24.46 | 220000 | 5.9809 | 0.6451 | 0.6132 |
| 0.1996 | 25.01 | 225000 | 6.1021 | 0.6389 | 0.6063 |
| 0.1966 | 25.57 | 230000 | 5.9612 | 0.6448 | 0.6140 |
| 0.1964 | 26.12 | 235000 | 6.0715 | 0.6434 | 0.6134 |
| 0.1971 | 26.68 | 240000 | 6.0237 | 0.6442 | 0.6127 |
| 0.1893 | 27.23 | 245000 | 6.0213 | 0.6418 | 0.6086 |
| 0.1891 | 27.79 | 250000 | 6.0386 | 0.6445 | 0.6127 |
| 0.1942 | 28.35 | 255000 | 6.0043 | 0.6428 | 0.6099 |
| 0.1966 | 28.9 | 260000 | 5.9983 | 0.6440 | 0.6130 |
| 0.1883 | 29.46 | 265000 | 6.0186 | 0.6461 | 0.6134 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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