transformers pytorch xlm-roberta generated_from_trainer dataset:massive base_model:haryoaw/scenario-TCR-data-cl-massive-all_1.1 license:mit endpoints_compatible region:us
---
license: mit
base_model: haryoaw/scenario-TCR-data-cl-massive-all_1.1
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
- f1
model-index:
- name: scenario-KD-PO-MSV-EN-CL-D2_data-en-massive_all_1_1
  results: []
---

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# scenario-KD-PO-MSV-EN-CL-D2_data-en-massive_all_1_1

This model is a fine-tuned version of [haryoaw/scenario-TCR-data-cl-massive-all_1.1](https://huggingface.co/haryoaw/scenario-TCR-data-cl-massive-all_1.1) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0528
- Accuracy: 0.6919
- F1: 0.6462

## 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: 47
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| No log        | 0.28  | 100   | 3.8138          | 0.6389   | 0.5611 |
| No log        | 0.56  | 200   | 3.4524          | 0.6604   | 0.5887 |
| No log        | 0.83  | 300   | 3.3073          | 0.6719   | 0.6070 |
| No log        | 1.11  | 400   | 3.3540          | 0.6642   | 0.6039 |
| 1.7741        | 1.39  | 500   | 3.2769          | 0.6714   | 0.6147 |
| 1.7741        | 1.67  | 600   | 3.3137          | 0.6690   | 0.6081 |
| 1.7741        | 1.94  | 700   | 3.2334          | 0.6758   | 0.6160 |
| 1.7741        | 2.22  | 800   | 3.4030          | 0.6625   | 0.6051 |
| 1.7741        | 2.5   | 900   | 3.3975          | 0.6681   | 0.6132 |
| 0.888         | 2.78  | 1000  | 3.2058          | 0.6820   | 0.6256 |
| 0.888         | 3.06  | 1100  | 3.2794          | 0.6768   | 0.6219 |
| 0.888         | 3.33  | 1200  | 3.1776          | 0.6826   | 0.6303 |
| 0.888         | 3.61  | 1300  | 3.2248          | 0.6753   | 0.6182 |
| 0.888         | 3.89  | 1400  | 3.1162          | 0.6857   | 0.6289 |
| 0.594         | 4.17  | 1500  | 3.2438          | 0.6794   | 0.6302 |
| 0.594         | 4.44  | 1600  | 3.2544          | 0.6827   | 0.6318 |
| 0.594         | 4.72  | 1700  | 3.2055          | 0.6806   | 0.6262 |
| 0.594         | 5.0   | 1800  | 3.2021          | 0.6812   | 0.6293 |
| 0.594         | 5.28  | 1900  | 3.1766          | 0.6862   | 0.6333 |
| 0.4608        | 5.56  | 2000  | 3.2701          | 0.6798   | 0.6284 |
| 0.4608        | 5.83  | 2100  | 3.2048          | 0.6827   | 0.6336 |
| 0.4608        | 6.11  | 2200  | 3.2235          | 0.6816   | 0.6297 |
| 0.4608        | 6.39  | 2300  | 3.1952          | 0.6814   | 0.6315 |
| 0.4608        | 6.67  | 2400  | 3.2690          | 0.6779   | 0.6257 |
| 0.392         | 6.94  | 2500  | 3.0575          | 0.6934   | 0.6372 |
| 0.392         | 7.22  | 2600  | 3.2603          | 0.6780   | 0.6287 |
| 0.392         | 7.5   | 2700  | 3.1213          | 0.6877   | 0.6360 |
| 0.392         | 7.78  | 2800  | 3.1437          | 0.6851   | 0.6325 |
| 0.392         | 8.06  | 2900  | 3.2481          | 0.6796   | 0.6277 |
| 0.3362        | 8.33  | 3000  | 3.2574          | 0.6805   | 0.6374 |
| 0.3362        | 8.61  | 3100  | 3.1799          | 0.6827   | 0.6353 |
| 0.3362        | 8.89  | 3200  | 3.1619          | 0.6831   | 0.6375 |
| 0.3362        | 9.17  | 3300  | 3.0926          | 0.6890   | 0.6431 |
| 0.3362        | 9.44  | 3400  | 3.2109          | 0.6808   | 0.6315 |
| 0.3053        | 9.72  | 3500  | 3.1810          | 0.6828   | 0.6339 |
| 0.3053        | 10.0  | 3600  | 3.3693          | 0.6738   | 0.6323 |
| 0.3053        | 10.28 | 3700  | 3.2051          | 0.6833   | 0.6330 |
| 0.3053        | 10.56 | 3800  | 3.0777          | 0.6892   | 0.6392 |
| 0.3053        | 10.83 | 3900  | 3.1487          | 0.6847   | 0.6343 |
| 0.2789        | 11.11 | 4000  | 3.1779          | 0.6859   | 0.6361 |
| 0.2789        | 11.39 | 4100  | 3.0622          | 0.6901   | 0.6418 |
| 0.2789        | 11.67 | 4200  | 3.2776          | 0.6778   | 0.6354 |
| 0.2789        | 11.94 | 4300  | 3.1611          | 0.6853   | 0.6401 |
| 0.2789        | 12.22 | 4400  | 3.1556          | 0.6875   | 0.6407 |
| 0.2647        | 12.5  | 4500  | 3.2092          | 0.6812   | 0.6368 |
| 0.2647        | 12.78 | 4600  | 3.3073          | 0.6782   | 0.6325 |
| 0.2647        | 13.06 | 4700  | 3.1741          | 0.6852   | 0.6419 |
| 0.2647        | 13.33 | 4800  | 3.1907          | 0.6847   | 0.6393 |
| 0.2647        | 13.61 | 4900  | 3.1004          | 0.6889   | 0.6454 |
| 0.2493        | 13.89 | 5000  | 3.0599          | 0.6910   | 0.6439 |
| 0.2493        | 14.17 | 5100  | 2.9828          | 0.6947   | 0.6466 |
| 0.2493        | 14.44 | 5200  | 3.0982          | 0.6898   | 0.6447 |
| 0.2493        | 14.72 | 5300  | 3.1316          | 0.6865   | 0.6404 |
| 0.2493        | 15.0  | 5400  | 3.1395          | 0.6867   | 0.6362 |
| 0.2411        | 15.28 | 5500  | 3.1417          | 0.6856   | 0.6400 |
| 0.2411        | 15.56 | 5600  | 3.1353          | 0.6860   | 0.6381 |
| 0.2411        | 15.83 | 5700  | 3.1166          | 0.6879   | 0.6394 |
| 0.2411        | 16.11 | 5800  | 3.0215          | 0.6937   | 0.6437 |
| 0.2411        | 16.39 | 5900  | 3.0632          | 0.6911   | 0.6443 |
| 0.23          | 16.67 | 6000  | 3.0764          | 0.6893   | 0.6406 |
| 0.23          | 16.94 | 6100  | 3.0606          | 0.6913   | 0.6437 |
| 0.23          | 17.22 | 6200  | 3.1023          | 0.6884   | 0.6407 |
| 0.23          | 17.5  | 6300  | 3.0954          | 0.6877   | 0.6393 |
| 0.23          | 17.78 | 6400  | 3.1082          | 0.6897   | 0.6434 |
| 0.2247        | 18.06 | 6500  | 3.2086          | 0.6836   | 0.6410 |
| 0.2247        | 18.33 | 6600  | 3.0756          | 0.6886   | 0.6441 |
| 0.2247        | 18.61 | 6700  | 3.1241          | 0.6869   | 0.6439 |
| 0.2247        | 18.89 | 6800  | 3.1416          | 0.6888   | 0.6433 |
| 0.2247        | 19.17 | 6900  | 3.0201          | 0.6941   | 0.6480 |
| 0.2145        | 19.44 | 7000  | 3.2236          | 0.6817   | 0.6393 |
| 0.2145        | 19.72 | 7100  | 3.1263          | 0.6892   | 0.6451 |
| 0.2145        | 20.0  | 7200  | 3.1594          | 0.6873   | 0.6419 |
| 0.2145        | 20.28 | 7300  | 3.1003          | 0.6880   | 0.6423 |
| 0.2145        | 20.56 | 7400  | 3.0484          | 0.6908   | 0.6449 |
| 0.2086        | 20.83 | 7500  | 3.0228          | 0.6939   | 0.6473 |
| 0.2086        | 21.11 | 7600  | 3.1254          | 0.6892   | 0.6430 |
| 0.2086        | 21.39 | 7700  | 3.0679          | 0.6916   | 0.6441 |
| 0.2086        | 21.67 | 7800  | 3.0992          | 0.6893   | 0.6428 |
| 0.2086        | 21.94 | 7900  | 3.0560          | 0.6906   | 0.6438 |
| 0.2035        | 22.22 | 8000  | 3.0692          | 0.6910   | 0.6461 |
| 0.2035        | 22.5  | 8100  | 3.1023          | 0.6874   | 0.6434 |
| 0.2035        | 22.78 | 8200  | 3.0447          | 0.6910   | 0.6469 |
| 0.2035        | 23.06 | 8300  | 3.0808          | 0.6902   | 0.6463 |
| 0.2035        | 23.33 | 8400  | 3.1031          | 0.6906   | 0.6464 |
| 0.1997        | 23.61 | 8500  | 3.0738          | 0.6901   | 0.6446 |
| 0.1997        | 23.89 | 8600  | 3.1039          | 0.6877   | 0.6409 |
| 0.1997        | 24.17 | 8700  | 3.0955          | 0.6894   | 0.6406 |
| 0.1997        | 24.44 | 8800  | 3.1115          | 0.6900   | 0.6455 |
| 0.1997        | 24.72 | 8900  | 3.1270          | 0.6870   | 0.6450 |
| 0.1946        | 25.0  | 9000  | 3.0710          | 0.6912   | 0.6487 |
| 0.1946        | 25.28 | 9100  | 3.0805          | 0.6904   | 0.6453 |
| 0.1946        | 25.56 | 9200  | 3.0624          | 0.6906   | 0.6466 |
| 0.1946        | 25.83 | 9300  | 3.0539          | 0.6916   | 0.6469 |
| 0.1946        | 26.11 | 9400  | 3.0886          | 0.6907   | 0.6463 |
| 0.1925        | 26.39 | 9500  | 3.0717          | 0.6905   | 0.6452 |
| 0.1925        | 26.67 | 9600  | 3.0957          | 0.6896   | 0.6455 |
| 0.1925        | 26.94 | 9700  | 3.0122          | 0.6945   | 0.6471 |
| 0.1925        | 27.22 | 9800  | 3.1008          | 0.6899   | 0.6471 |
| 0.1925        | 27.5  | 9900  | 3.0804          | 0.6910   | 0.6462 |
| 0.1901        | 27.78 | 10000 | 3.0287          | 0.6930   | 0.6483 |
| 0.1901        | 28.06 | 10100 | 3.0691          | 0.6900   | 0.6452 |
| 0.1901        | 28.33 | 10200 | 3.0444          | 0.6925   | 0.6468 |
| 0.1901        | 28.61 | 10300 | 3.0686          | 0.6909   | 0.6470 |
| 0.1901        | 28.89 | 10400 | 3.0778          | 0.6908   | 0.6462 |
| 0.189         | 29.17 | 10500 | 3.0605          | 0.6912   | 0.6462 |
| 0.189         | 29.44 | 10600 | 3.0580          | 0.6918   | 0.6469 |
| 0.189         | 29.72 | 10700 | 3.0559          | 0.6924   | 0.6471 |
| 0.189         | 30.0  | 10800 | 3.0528          | 0.6919   | 0.6462 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
