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

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# scenario-KD-PO-MSV-EN-EN-D2_data-en-massive_all_1_1

This model is a fine-tuned version of [haryoaw/scenario-TCR_data-en-massive_all_1_1](https://huggingface.co/haryoaw/scenario-TCR_data-en-massive_all_1_1) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7908
- Accuracy: 0.5365
- F1: 0.4786

## 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   | 5.1614          | 0.4860   | 0.3990 |
| No log        | 0.56  | 200   | 5.1590          | 0.4790   | 0.4068 |
| No log        | 0.83  | 300   | 4.9490          | 0.5093   | 0.4251 |
| No log        | 1.11  | 400   | 5.0823          | 0.5050   | 0.4359 |
| 2.0111        | 1.39  | 500   | 5.9727          | 0.4599   | 0.4095 |
| 2.0111        | 1.67  | 600   | 5.0874          | 0.5192   | 0.4413 |
| 2.0111        | 1.94  | 700   | 5.2264          | 0.5153   | 0.4446 |
| 2.0111        | 2.22  | 800   | 5.8439          | 0.4731   | 0.4246 |
| 2.0111        | 2.5   | 900   | 5.5183          | 0.5102   | 0.4374 |
| 1.0011        | 2.78  | 1000  | 6.6020          | 0.4541   | 0.4114 |
| 1.0011        | 3.06  | 1100  | 6.1540          | 0.4795   | 0.4246 |
| 1.0011        | 3.33  | 1200  | 5.5901          | 0.5168   | 0.4453 |
| 1.0011        | 3.61  | 1300  | 5.9977          | 0.4949   | 0.4239 |
| 1.0011        | 3.89  | 1400  | 5.7634          | 0.5070   | 0.4461 |
| 0.67          | 4.17  | 1500  | 5.6536          | 0.5233   | 0.4470 |
| 0.67          | 4.44  | 1600  | 5.8327          | 0.5184   | 0.4445 |
| 0.67          | 4.72  | 1700  | 5.8242          | 0.5181   | 0.4379 |
| 0.67          | 5.0   | 1800  | 6.5490          | 0.4856   | 0.4429 |
| 0.67          | 5.28  | 1900  | 5.6837          | 0.5270   | 0.4480 |
| 0.4793        | 5.56  | 2000  | 5.7311          | 0.5269   | 0.4600 |
| 0.4793        | 5.83  | 2100  | 5.8170          | 0.5244   | 0.4530 |
| 0.4793        | 6.11  | 2200  | 5.9569          | 0.5190   | 0.4572 |
| 0.4793        | 6.39  | 2300  | 5.7950          | 0.5260   | 0.4610 |
| 0.4793        | 6.67  | 2400  | 6.8529          | 0.4865   | 0.4238 |
| 0.3811        | 6.94  | 2500  | 6.3352          | 0.5007   | 0.4455 |
| 0.3811        | 7.22  | 2600  | 6.1439          | 0.5193   | 0.4511 |
| 0.3811        | 7.5   | 2700  | 5.9568          | 0.5255   | 0.4573 |
| 0.3811        | 7.78  | 2800  | 6.8359          | 0.4888   | 0.4367 |
| 0.3811        | 8.06  | 2900  | 6.1294          | 0.5196   | 0.4600 |
| 0.3163        | 8.33  | 3000  | 6.5122          | 0.5033   | 0.4433 |
| 0.3163        | 8.61  | 3100  | 6.0960          | 0.5186   | 0.4621 |
| 0.3163        | 8.89  | 3200  | 6.4174          | 0.5053   | 0.4457 |
| 0.3163        | 9.17  | 3300  | 6.3298          | 0.5052   | 0.4527 |
| 0.3163        | 9.44  | 3400  | 6.7517          | 0.4944   | 0.4515 |
| 0.2726        | 9.72  | 3500  | 6.6248          | 0.5012   | 0.4521 |
| 0.2726        | 10.0  | 3600  | 6.8595          | 0.4902   | 0.4429 |
| 0.2726        | 10.28 | 3700  | 6.2875          | 0.5141   | 0.4546 |
| 0.2726        | 10.56 | 3800  | 6.0717          | 0.5228   | 0.4625 |
| 0.2726        | 10.83 | 3900  | 5.9079          | 0.5230   | 0.4608 |
| 0.2407        | 11.11 | 4000  | 5.9972          | 0.5251   | 0.4553 |
| 0.2407        | 11.39 | 4100  | 6.0573          | 0.5248   | 0.4521 |
| 0.2407        | 11.67 | 4200  | 6.3872          | 0.5113   | 0.4610 |
| 0.2407        | 11.94 | 4300  | 6.3037          | 0.5159   | 0.4547 |
| 0.2407        | 12.22 | 4400  | 6.2782          | 0.5123   | 0.4532 |
| 0.2201        | 12.5  | 4500  | 6.1318          | 0.5239   | 0.4619 |
| 0.2201        | 12.78 | 4600  | 6.2953          | 0.5150   | 0.4622 |
| 0.2201        | 13.06 | 4700  | 6.2881          | 0.5212   | 0.4625 |
| 0.2201        | 13.33 | 4800  | 6.4247          | 0.5104   | 0.4571 |
| 0.2201        | 13.61 | 4900  | 6.1305          | 0.5250   | 0.4635 |
| 0.2044        | 13.89 | 5000  | 5.8602          | 0.5343   | 0.4711 |
| 0.2044        | 14.17 | 5100  | 5.8894          | 0.5289   | 0.4659 |
| 0.2044        | 14.44 | 5200  | 6.2145          | 0.5211   | 0.4625 |
| 0.2044        | 14.72 | 5300  | 6.1177          | 0.5234   | 0.4620 |
| 0.2044        | 15.0  | 5400  | 5.8386          | 0.5324   | 0.4677 |
| 0.1962        | 15.28 | 5500  | 6.0351          | 0.5254   | 0.4673 |
| 0.1962        | 15.56 | 5600  | 5.9421          | 0.5290   | 0.4679 |
| 0.1962        | 15.83 | 5700  | 5.9836          | 0.5239   | 0.4673 |
| 0.1962        | 16.11 | 5800  | 6.0245          | 0.5304   | 0.4725 |
| 0.1962        | 16.39 | 5900  | 6.3386          | 0.5162   | 0.4641 |
| 0.1876        | 16.67 | 6000  | 6.2542          | 0.5185   | 0.4641 |
| 0.1876        | 16.94 | 6100  | 5.9417          | 0.5308   | 0.4709 |
| 0.1876        | 17.22 | 6200  | 5.7436          | 0.5418   | 0.4799 |
| 0.1876        | 17.5  | 6300  | 6.1042          | 0.5311   | 0.4715 |
| 0.1876        | 17.78 | 6400  | 6.2918          | 0.5155   | 0.4654 |
| 0.179         | 18.06 | 6500  | 6.1913          | 0.5178   | 0.4609 |
| 0.179         | 18.33 | 6600  | 6.0804          | 0.5316   | 0.4724 |
| 0.179         | 18.61 | 6700  | 5.8362          | 0.5330   | 0.4722 |
| 0.179         | 18.89 | 6800  | 6.1539          | 0.5254   | 0.4730 |
| 0.179         | 19.17 | 6900  | 6.2638          | 0.5155   | 0.4655 |
| 0.1738        | 19.44 | 7000  | 5.9394          | 0.5344   | 0.4739 |
| 0.1738        | 19.72 | 7100  | 5.9005          | 0.5341   | 0.4749 |
| 0.1738        | 20.0  | 7200  | 6.4484          | 0.5119   | 0.4635 |
| 0.1738        | 20.28 | 7300  | 5.9396          | 0.5268   | 0.4674 |
| 0.1738        | 20.56 | 7400  | 5.9318          | 0.5286   | 0.4688 |
| 0.1694        | 20.83 | 7500  | 6.0616          | 0.5255   | 0.4723 |
| 0.1694        | 21.11 | 7600  | 5.8511          | 0.5351   | 0.4742 |
| 0.1694        | 21.39 | 7700  | 5.9842          | 0.5282   | 0.4692 |
| 0.1694        | 21.67 | 7800  | 6.2691          | 0.5176   | 0.4647 |
| 0.1694        | 21.94 | 7900  | 6.1831          | 0.5199   | 0.4635 |
| 0.1642        | 22.22 | 8000  | 5.8385          | 0.5363   | 0.4748 |
| 0.1642        | 22.5  | 8100  | 6.1696          | 0.5195   | 0.4643 |
| 0.1642        | 22.78 | 8200  | 5.7393          | 0.5382   | 0.4778 |
| 0.1642        | 23.06 | 8300  | 5.6763          | 0.5385   | 0.4752 |
| 0.1642        | 23.33 | 8400  | 5.7960          | 0.5368   | 0.4756 |
| 0.1635        | 23.61 | 8500  | 5.8562          | 0.5341   | 0.4738 |
| 0.1635        | 23.89 | 8600  | 5.9408          | 0.5322   | 0.4707 |
| 0.1635        | 24.17 | 8700  | 5.6433          | 0.5433   | 0.4774 |
| 0.1635        | 24.44 | 8800  | 5.7061          | 0.5391   | 0.4779 |
| 0.1635        | 24.72 | 8900  | 5.6887          | 0.5407   | 0.4809 |
| 0.1587        | 25.0  | 9000  | 5.8796          | 0.5328   | 0.4742 |
| 0.1587        | 25.28 | 9100  | 5.7879          | 0.5370   | 0.4750 |
| 0.1587        | 25.56 | 9200  | 5.7480          | 0.5348   | 0.4766 |
| 0.1587        | 25.83 | 9300  | 5.9260          | 0.5292   | 0.4738 |
| 0.1587        | 26.11 | 9400  | 5.7467          | 0.5347   | 0.4752 |
| 0.1565        | 26.39 | 9500  | 5.9216          | 0.5324   | 0.4739 |
| 0.1565        | 26.67 | 9600  | 5.7947          | 0.5350   | 0.4769 |
| 0.1565        | 26.94 | 9700  | 5.7898          | 0.5349   | 0.4757 |
| 0.1565        | 27.22 | 9800  | 5.9008          | 0.5298   | 0.4736 |
| 0.1565        | 27.5  | 9900  | 5.6786          | 0.5406   | 0.4779 |
| 0.1542        | 27.78 | 10000 | 5.7844          | 0.5350   | 0.4751 |
| 0.1542        | 28.06 | 10100 | 5.7515          | 0.5365   | 0.4758 |
| 0.1542        | 28.33 | 10200 | 5.8749          | 0.5315   | 0.4739 |
| 0.1542        | 28.61 | 10300 | 5.8789          | 0.5320   | 0.4756 |
| 0.1542        | 28.89 | 10400 | 5.7866          | 0.5351   | 0.4769 |
| 0.1533        | 29.17 | 10500 | 5.7045          | 0.5390   | 0.4799 |
| 0.1533        | 29.44 | 10600 | 5.7853          | 0.5356   | 0.4760 |
| 0.1533        | 29.72 | 10700 | 5.7948          | 0.5354   | 0.4771 |
| 0.1533        | 30.0  | 10800 | 5.7908          | 0.5365   | 0.4786 |


### Framework versions

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