---
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