pytorch encoder-decoder text2text-generation transformers generated_from_trainer model-index autotrain_compatible
---
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: rob2rand_chen_w_prefix_tc
  results: []
---

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# rob2rand_chen_w_prefix_tc

This model is a fine-tuned version of [imamnurby/rob2rand_chen_w_prefix](https://huggingface.co/imamnurby/rob2rand_chen_w_prefix) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2749
- Bleu: 83.9120
- Em: 86.2159
- Bleu Em: 85.0639

## 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-06
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Em      | Bleu Em |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|
| 0.6922        | 0.71  | 500   | 0.2425          | 68.5819 | 79.7927 | 74.1873 |
| 0.086         | 1.42  | 1000  | 0.2480          | 70.9791 | 79.5855 | 75.2823 |
| 0.0865        | 2.13  | 1500  | 0.2567          | 68.7037 | 78.8256 | 73.7646 |
| 0.0758        | 2.84  | 2000  | 0.2483          | 69.4605 | 80.2418 | 74.8512 |
| 0.0683        | 3.55  | 2500  | 0.2662          | 68.3732 | 78.4456 | 73.4094 |
| 0.0643        | 4.26  | 3000  | 0.2700          | 66.5413 | 78.3765 | 72.4589 |
| 0.0596        | 4.97  | 3500  | 0.2611          | 67.4313 | 78.9637 | 73.1975 |
| 0.0519        | 5.68  | 4000  | 0.2697          | 68.3717 | 79.1019 | 73.7368 |
| 0.0478        | 6.39  | 4500  | 0.2914          | 69.7507 | 77.7202 | 73.7354 |
| 0.0461        | 7.1   | 5000  | 0.2776          | 68.5387 | 79.1019 | 73.8203 |
| 0.04          | 7.81  | 5500  | 0.2975          | 67.6316 | 78.1693 | 72.9004 |
| 0.0373        | 8.52  | 6000  | 0.2922          | 68.0161 | 79.4473 | 73.7317 |
| 0.0345        | 9.23  | 6500  | 0.3032          | 69.4580 | 79.2401 | 74.3490 |
| 0.032         | 9.94  | 7000  | 0.3104          | 67.2595 | 79.0328 | 73.1462 |
| 0.0294        | 10.65 | 7500  | 0.3077          | 65.8142 | 78.4801 | 72.1472 |
| 0.0269        | 11.36 | 8000  | 0.3092          | 70.2072 | 78.8601 | 74.5337 |
| 0.026         | 12.07 | 8500  | 0.3117          | 70.4504 | 79.4473 | 74.9489 |
| 0.0229        | 12.78 | 9000  | 0.3114          | 69.4635 | 79.2401 | 74.3518 |
| 0.0215        | 13.49 | 9500  | 0.3143          | 67.3601 | 79.3092 | 73.3346 |
| 0.0205        | 14.2  | 10000 | 0.3176          | 68.4031 | 78.9983 | 73.7007 |
| 0.0195        | 14.91 | 10500 | 0.3253          | 66.5673 | 78.9637 | 72.7655 |
| 0.0173        | 15.62 | 11000 | 0.3377          | 68.7553 | 78.7219 | 73.7386 |
| 0.0164        | 16.34 | 11500 | 0.3377          | 69.2474 | 79.1364 | 74.1919 |
| 0.0161        | 17.05 | 12000 | 0.3371          | 69.0846 | 79.6200 | 74.3523 |
| 0.0148        | 17.76 | 12500 | 0.3457          | 70.8330 | 79.3782 | 75.1056 |
| 0.0137        | 18.47 | 13000 | 0.3516          | 69.5576 | 79.2401 | 74.3988 |
| 0.0135        | 19.18 | 13500 | 0.3573          | 70.3232 | 79.1364 | 74.7298 |
| 0.0127        | 19.89 | 14000 | 0.3574          | 70.2481 | 79.1019 | 74.6750 |
| 0.0115        | 20.6  | 14500 | 0.3694          | 65.7587 | 78.3765 | 72.0676 |
| 0.0107        | 21.31 | 15000 | 0.3696          | 68.7923 | 78.5838 | 73.6880 |
| 0.0107        | 22.02 | 15500 | 0.3607          | 69.4452 | 78.8256 | 74.1354 |
| 0.0101        | 22.73 | 16000 | 0.3770          | 68.6731 | 78.5492 | 73.6112 |
| 0.0095        | 23.44 | 16500 | 0.3648          | 69.8402 | 79.7237 | 74.7819 |
| 0.0088        | 24.15 | 17000 | 0.3822          | 69.6238 | 79.0328 | 74.3283 |
| 0.0088        | 24.86 | 17500 | 0.3816          | 68.5422 | 79.1364 | 73.8393 |
| 0.0079        | 25.57 | 18000 | 0.3822          | 69.1359 | 79.2401 | 74.1880 |
| 0.0073        | 26.28 | 18500 | 0.3742          | 69.8331 | 79.6891 | 74.7611 |
| 0.007         | 26.99 | 19000 | 0.3849          | 69.5048 | 79.2746 | 74.3897 |
| 0.0072        | 27.7  | 19500 | 0.3881          | 69.6135 | 79.2055 | 74.4095 |
| 0.0059        | 28.41 | 20000 | 0.3922          | 70.2656 | 79.2746 | 74.7701 |
| 0.0069        | 29.12 | 20500 | 0.3936          | 68.2044 | 78.7910 | 73.4977 |
| 0.0059        | 29.83 | 21000 | 0.3983          | 69.6257 | 79.4473 | 74.5365 |
| 0.0055        | 30.54 | 21500 | 0.3973          | 70.4039 | 79.5509 | 74.9774 |
| 0.0057        | 31.25 | 22000 | 0.3960          | 70.3015 | 79.6546 | 74.9780 |
| 0.0056        | 31.96 | 22500 | 0.3945          | 69.9785 | 79.5855 | 74.7820 |
| 0.0049        | 32.67 | 23000 | 0.3947          | 70.1822 | 79.6546 | 74.9184 |
| 0.0049        | 33.38 | 23500 | 0.3957          | 69.1207 | 79.3437 | 74.2322 |
| 0.0048        | 34.09 | 24000 | 0.4097          | 68.8815 | 78.9292 | 73.9053 |
| 0.0043        | 34.8  | 24500 | 0.4039          | 70.0982 | 79.4473 | 74.7727 |
| 0.0044        | 35.51 | 25000 | 0.4080          | 69.3472 | 79.5164 | 74.4318 |
| 0.0042        | 36.22 | 25500 | 0.4066          | 69.0213 | 79.0674 | 74.0443 |
| 0.0038        | 36.93 | 26000 | 0.4128          | 69.1452 | 79.3092 | 74.2272 |
| 0.0037        | 37.64 | 26500 | 0.4134          | 69.2672 | 79.5164 | 74.3918 |
| 0.0034        | 38.35 | 27000 | 0.4161          | 69.7751 | 79.5509 | 74.6630 |
| 0.0038        | 39.06 | 27500 | 0.4037          | 69.4092 | 79.6546 | 74.5319 |
| 0.0031        | 39.77 | 28000 | 0.4041          | 69.3912 | 79.6546 | 74.5229 |
| 0.0032        | 40.48 | 28500 | 0.4185          | 69.1159 | 79.4473 | 74.2816 |
| 0.0031        | 41.19 | 29000 | 0.4245          | 68.6867 | 78.9983 | 73.8425 |
| 0.003         | 41.9  | 29500 | 0.4202          | 69.4091 | 79.3092 | 74.3591 |
| 0.0027        | 42.61 | 30000 | 0.4249          | 68.7400 | 79.0328 | 73.8864 |
| 0.0026        | 43.32 | 30500 | 0.4175          | 69.9729 | 79.8273 | 74.9001 |
| 0.0027        | 44.03 | 31000 | 0.4189          | 69.6688 | 79.5855 | 74.6271 |
| 0.0027        | 44.74 | 31500 | 0.4203          | 69.4071 | 79.5855 | 74.4963 |
| 0.0025        | 45.45 | 32000 | 0.4265          | 69.3197 | 79.1019 | 74.2108 |
| 0.0023        | 46.16 | 32500 | 0.4255          | 69.7513 | 79.3437 | 74.5475 |
| 0.0023        | 46.88 | 33000 | 0.4227          | 69.2893 | 79.5509 | 74.4201 |
| 0.0023        | 47.59 | 33500 | 0.4233          | 69.6060 | 79.5509 | 74.5785 |
| 0.002         | 48.3  | 34000 | 0.4239          | 69.0113 | 79.4819 | 74.2466 |
| 0.0024        | 49.01 | 34500 | 0.4239          | 68.9754 | 79.4128 | 74.1941 |
| 0.0019        | 49.72 | 35000 | 0.4228          | 68.9220 | 79.3782 | 74.1501 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.7.1
- Datasets 2.1.0
- Tokenizers 0.12.1
