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	---
	tags:
	- generated_from_trainer
	metrics:
	- bleu
	model-index:
	- name: rob2rand_chen_w_prefix_tc
	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. -->

	# 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

	---
	tags:
	- generated_from_trainer
	metrics:
	- bleu
	model-index:
	- name: rob2rand_chen_w_prefix_tc
	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. -->

	# 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