skilltext / README.md

Training in progress, step 500

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	---
	base_model: ai-forever/ruT5-base
	tags:
	- generated_from_trainer
	metrics:
	- rouge
	- bleu
	model-index:
	- name: skilltext
	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. -->

	# skilltext

	This model is a fine-tuned version of [ai-forever/ruT5-base](https://huggingface.co/ai-forever/ruT5-base) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.0396
	- Rouge1: 35.5496
	- Rouge2: 22.9927
	- Rougel: 33.7986
	- Rougelsum: 33.9427
	- Bleu: 3.0002
	- Gen Len: 18.7273

	## 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: 2e-05
	- train_batch_size: 1
	- eval_batch_size: 1
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 30
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Bleu \| Gen Len \|
	\|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:------:\|:-------:\|
	\| No log \| 0.5882 \| 50 \| 2.0006 \| 22.8478 \| 9.3528 \| 21.5245 \| 21.4195 \| 1.3965 \| 19.0 \|
	\| No log \| 1.1765 \| 100 \| 1.5029 \| 26.0894 \| 12.5184 \| 22.7242 \| 22.8568 \| 1.7386 \| 18.9545 \|
	\| No log \| 1.7647 \| 150 \| 1.4072 \| 24.1385 \| 9.8714 \| 22.0278 \| 22.0679 \| 2.009 \| 18.9545 \|
	\| No log \| 2.3529 \| 200 \| 1.3292 \| 27.642 \| 12.2998 \| 26.3455 \| 25.9994 \| 1.2632 \| 18.7727 \|
	\| No log \| 2.9412 \| 250 \| 1.2788 \| 32.096 \| 12.3806 \| 30.9883 \| 30.6962 \| 1.6429 \| 18.7273 \|
	\| No log \| 3.5294 \| 300 \| 1.1847 \| 31.8602 \| 21.2094 \| 31.1454 \| 30.9145 \| 1.5913 \| 18.8636 \|
	\| No log \| 4.1176 \| 350 \| 1.2193 \| 22.6777 \| 11.7225 \| 22.1941 \| 22.1638 \| 1.4306 \| 18.7727 \|
	\| No log \| 4.7059 \| 400 \| 1.1527 \| 23.4161 \| 11.2979 \| 22.9918 \| 23.0266 \| 1.7552 \| 18.8636 \|
	\| No log \| 5.2941 \| 450 \| 1.1200 \| 28.9205 \| 15.5233 \| 27.153 \| 27.2644 \| 1.8557 \| 18.7273 \|
	\| 2.1495 \| 5.8824 \| 500 \| 1.1426 \| 28.2199 \| 13.8386 \| 26.9115 \| 26.5472 \| 2.3855 \| 18.7273 \|
	\| 2.1495 \| 6.4706 \| 550 \| 1.1053 \| 32.432 \| 18.9395 \| 30.9397 \| 31.1198 \| 2.2867 \| 18.7727 \|
	\| 2.1495 \| 7.0588 \| 600 \| 1.0777 \| 38.285 \| 23.5443 \| 35.0994 \| 35.3165 \| 2.6353 \| 18.7727 \|
	\| 2.1495 \| 7.6471 \| 650 \| 1.0900 \| 38.5934 \| 21.6941 \| 36.5629 \| 36.9151 \| 2.2212 \| 18.7727 \|
	\| 2.1495 \| 8.2353 \| 700 \| 1.0931 \| 41.2586 \| 27.5923 \| 40.1612 \| 40.1672 \| 2.5568 \| 18.8182 \|
	\| 2.1495 \| 8.8235 \| 750 \| 1.0691 \| 38.3785 \| 25.0231 \| 38.453 \| 38.5248 \| 2.4491 \| 18.7273 \|
	\| 2.1495 \| 9.4118 \| 800 \| 1.0627 \| 36.3073 \| 20.703 \| 35.2405 \| 35.3787 \| 2.3678 \| 18.8636 \|
	\| 2.1495 \| 10.0 \| 850 \| 1.0528 \| 39.1894 \| 24.8355 \| 39.3713 \| 39.483 \| 1.9687 \| 18.8636 \|
	\| 2.1495 \| 10.5882 \| 900 \| 1.0628 \| 40.0052 \| 23.746 \| 38.8726 \| 39.077 \| 2.0485 \| 18.8636 \|
	\| 2.1495 \| 11.1765 \| 950 \| 1.0371 \| 34.4982 \| 23.4663 \| 34.1685 \| 34.1247 \| 2.0922 \| 18.8636 \|
	\| 1.046 \| 11.7647 \| 1000 \| 1.0368 \| 38.0619 \| 19.7898 \| 36.4367 \| 36.8115 \| 2.3387 \| 18.8636 \|
	\| 1.046 \| 12.3529 \| 1050 \| 1.0427 \| 38.9055 \| 25.1615 \| 38.8253 \| 38.9385 \| 2.5522 \| 18.8182 \|
	\| 1.046 \| 12.9412 \| 1100 \| 1.0255 \| 36.5256 \| 21.2328 \| 34.8816 \| 35.2236 \| 2.4057 \| 18.8182 \|
	\| 1.046 \| 13.5294 \| 1150 \| 1.0237 \| 36.0048 \| 25.3977 \| 35.9471 \| 35.9807 \| 2.4804 \| 18.8182 \|
	\| 1.046 \| 14.1176 \| 1200 \| 0.9918 \| 32.6697 \| 21.3968 \| 30.8639 \| 31.0221 \| 2.4669 \| 18.7727 \|
	\| 1.046 \| 14.7059 \| 1250 \| 1.0598 \| 37.7878 \| 20.6971 \| 36.6794 \| 36.7289 \| 2.5767 \| 18.7727 \|
	\| 1.046 \| 15.2941 \| 1300 \| 1.0130 \| 34.549 \| 24.4177 \| 34.0376 \| 34.1226 \| 2.1773 \| 18.8182 \|
	\| 1.046 \| 15.8824 \| 1350 \| 1.0256 \| 32.774 \| 19.6047 \| 31.6125 \| 31.9067 \| 2.0504 \| 18.7727 \|
	\| 1.046 \| 16.4706 \| 1400 \| 1.0232 \| 31.4885 \| 18.4703 \| 30.0937 \| 30.5529 \| 2.514 \| 18.8182 \|
	\| 1.046 \| 17.0588 \| 1450 \| 1.0210 \| 33.4684 \| 20.7982 \| 31.7789 \| 32.0023 \| 2.4881 \| 18.7273 \|
	\| 0.7674 \| 17.6471 \| 1500 \| 1.0419 \| 37.4914 \| 20.9444 \| 35.0519 \| 35.2368 \| 3.0058 \| 18.7727 \|
	\| 0.7674 \| 18.2353 \| 1550 \| 1.0328 \| 36.5606 \| 21.0215 \| 35.2548 \| 35.4748 \| 2.7878 \| 18.7273 \|
	\| 0.7674 \| 18.8235 \| 1600 \| 1.0376 \| 31.3516 \| 18.5826 \| 29.6759 \| 29.8435 \| 2.3192 \| 18.8182 \|
	\| 0.7674 \| 19.4118 \| 1650 \| 1.0414 \| 37.4725 \| 22.3216 \| 35.6306 \| 35.7383 \| 2.477 \| 18.8182 \|
	\| 0.7674 \| 20.0 \| 1700 \| 1.0513 \| 39.5759 \| 23.2665 \| 39.2332 \| 39.3667 \| 2.4322 \| 18.7273 \|
	\| 0.7674 \| 20.5882 \| 1750 \| 1.0518 \| 36.1526 \| 23.8263 \| 34.5677 \| 34.6173 \| 2.8518 \| 18.7727 \|
	\| 0.7674 \| 21.1765 \| 1800 \| 1.0446 \| 41.5192 \| 23.3064 \| 39.3799 \| 39.6548 \| 3.0326 \| 18.8182 \|
	\| 0.7674 \| 21.7647 \| 1850 \| 1.0150 \| 40.5093 \| 21.8683 \| 38.2773 \| 38.6063 \| 2.6653 \| 18.8636 \|
	\| 0.7674 \| 22.3529 \| 1900 \| 1.0364 \| 34.2216 \| 20.2095 \| 32.5945 \| 32.6999 \| 2.6078 \| 18.8182 \|
	\| 0.7674 \| 22.9412 \| 1950 \| 1.0148 \| 39.8173 \| 20.6247 \| 37.2954 \| 37.6752 \| 3.0336 \| 18.8636 \|
	\| 0.6485 \| 23.5294 \| 2000 \| 1.0429 \| 40.2889 \| 21.1598 \| 37.7657 \| 38.0596 \| 2.9108 \| 18.8182 \|
	\| 0.6485 \| 24.1176 \| 2050 \| 1.0423 \| 39.2679 \| 20.8842 \| 36.7395 \| 36.9295 \| 2.845 \| 18.8636 \|
	\| 0.6485 \| 24.7059 \| 2100 \| 1.0358 \| 39.086 \| 20.7799 \| 36.2138 \| 36.3741 \| 2.9429 \| 18.8182 \|
	\| 0.6485 \| 25.2941 \| 2150 \| 1.0219 \| 38.754 \| 22.4097 \| 36.9752 \| 37.121 \| 2.831 \| 18.8182 \|
	\| 0.6485 \| 25.8824 \| 2200 \| 1.0450 \| 38.3531 \| 22.3593 \| 36.4439 \| 36.6304 \| 2.9804 \| 18.7727 \|
	\| 0.6485 \| 26.4706 \| 2250 \| 1.0482 \| 40.6921 \| 23.617 \| 39.298 \| 39.5895 \| 3.0971 \| 18.7727 \|
	\| 0.6485 \| 27.0588 \| 2300 \| 1.0495 \| 39.6761 \| 22.7969 \| 37.0805 \| 37.4949 \| 3.2639 \| 18.7727 \|
	\| 0.6485 \| 27.6471 \| 2350 \| 1.0412 \| 40.8199 \| 23.7109 \| 38.9222 \| 39.2493 \| 3.0267 \| 18.7273 \|
	\| 0.6485 \| 28.2353 \| 2400 \| 1.0453 \| 39.9504 \| 23.888 \| 38.0725 \| 38.3121 \| 3.2191 \| 18.7727 \|
	\| 0.6485 \| 28.8235 \| 2450 \| 1.0400 \| 36.205 \| 23.1356 \| 34.6087 \| 34.6263 \| 3.028 \| 18.7727 \|
	\| 0.5501 \| 29.4118 \| 2500 \| 1.0402 \| 35.033 \| 22.2393 \| 33.3754 \| 33.4477 \| 3.0299 \| 18.7273 \|
	\| 0.5501 \| 30.0 \| 2550 \| 1.0396 \| 35.5496 \| 22.9927 \| 33.7986 \| 33.9427 \| 3.0002 \| 18.7273 \|


	### Framework versions

	- Transformers 4.40.0
	- Pytorch 2.2.2
	- Datasets 2.12.0
	- Tokenizers 0.19.1

	---
	base_model: ai-forever/ruT5-base
	tags:
	- generated_from_trainer
	metrics:
	- rouge
	- bleu
	model-index:
	- name: skilltext
	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. -->

	# skilltext

	This model is a fine-tuned version of [ai-forever/ruT5-base](https://huggingface.co/ai-forever/ruT5-base) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.0396
	- Rouge1: 35.5496
	- Rouge2: 22.9927
	- Rougel: 33.7986
	- Rougelsum: 33.9427
	- Bleu: 3.0002
	- Gen Len: 18.7273

	## 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: 2e-05
	- train_batch_size: 1
	- eval_batch_size: 1
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 30
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Bleu \| Gen Len \|
	\|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:------:\|:-------:\|
	\| No log \| 0.5882 \| 50 \| 2.0006 \| 22.8478 \| 9.3528 \| 21.5245 \| 21.4195 \| 1.3965 \| 19.0 \|
	\| No log \| 1.1765 \| 100 \| 1.5029 \| 26.0894 \| 12.5184 \| 22.7242 \| 22.8568 \| 1.7386 \| 18.9545 \|
	\| No log \| 1.7647 \| 150 \| 1.4072 \| 24.1385 \| 9.8714 \| 22.0278 \| 22.0679 \| 2.009 \| 18.9545 \|
	\| No log \| 2.3529 \| 200 \| 1.3292 \| 27.642 \| 12.2998 \| 26.3455 \| 25.9994 \| 1.2632 \| 18.7727 \|
	\| No log \| 2.9412 \| 250 \| 1.2788 \| 32.096 \| 12.3806 \| 30.9883 \| 30.6962 \| 1.6429 \| 18.7273 \|
	\| No log \| 3.5294 \| 300 \| 1.1847 \| 31.8602 \| 21.2094 \| 31.1454 \| 30.9145 \| 1.5913 \| 18.8636 \|
	\| No log \| 4.1176 \| 350 \| 1.2193 \| 22.6777 \| 11.7225 \| 22.1941 \| 22.1638 \| 1.4306 \| 18.7727 \|
	\| No log \| 4.7059 \| 400 \| 1.1527 \| 23.4161 \| 11.2979 \| 22.9918 \| 23.0266 \| 1.7552 \| 18.8636 \|
	\| No log \| 5.2941 \| 450 \| 1.1200 \| 28.9205 \| 15.5233 \| 27.153 \| 27.2644 \| 1.8557 \| 18.7273 \|
	\| 2.1495 \| 5.8824 \| 500 \| 1.1426 \| 28.2199 \| 13.8386 \| 26.9115 \| 26.5472 \| 2.3855 \| 18.7273 \|
	\| 2.1495 \| 6.4706 \| 550 \| 1.1053 \| 32.432 \| 18.9395 \| 30.9397 \| 31.1198 \| 2.2867 \| 18.7727 \|
	\| 2.1495 \| 7.0588 \| 600 \| 1.0777 \| 38.285 \| 23.5443 \| 35.0994 \| 35.3165 \| 2.6353 \| 18.7727 \|
	\| 2.1495 \| 7.6471 \| 650 \| 1.0900 \| 38.5934 \| 21.6941 \| 36.5629 \| 36.9151 \| 2.2212 \| 18.7727 \|
	\| 2.1495 \| 8.2353 \| 700 \| 1.0931 \| 41.2586 \| 27.5923 \| 40.1612 \| 40.1672 \| 2.5568 \| 18.8182 \|
	\| 2.1495 \| 8.8235 \| 750 \| 1.0691 \| 38.3785 \| 25.0231 \| 38.453 \| 38.5248 \| 2.4491 \| 18.7273 \|
	\| 2.1495 \| 9.4118 \| 800 \| 1.0627 \| 36.3073 \| 20.703 \| 35.2405 \| 35.3787 \| 2.3678 \| 18.8636 \|
	\| 2.1495 \| 10.0 \| 850 \| 1.0528 \| 39.1894 \| 24.8355 \| 39.3713 \| 39.483 \| 1.9687 \| 18.8636 \|
	\| 2.1495 \| 10.5882 \| 900 \| 1.0628 \| 40.0052 \| 23.746 \| 38.8726 \| 39.077 \| 2.0485 \| 18.8636 \|
	\| 2.1495 \| 11.1765 \| 950 \| 1.0371 \| 34.4982 \| 23.4663 \| 34.1685 \| 34.1247 \| 2.0922 \| 18.8636 \|
	\| 1.046 \| 11.7647 \| 1000 \| 1.0368 \| 38.0619 \| 19.7898 \| 36.4367 \| 36.8115 \| 2.3387 \| 18.8636 \|
	\| 1.046 \| 12.3529 \| 1050 \| 1.0427 \| 38.9055 \| 25.1615 \| 38.8253 \| 38.9385 \| 2.5522 \| 18.8182 \|
	\| 1.046 \| 12.9412 \| 1100 \| 1.0255 \| 36.5256 \| 21.2328 \| 34.8816 \| 35.2236 \| 2.4057 \| 18.8182 \|
	\| 1.046 \| 13.5294 \| 1150 \| 1.0237 \| 36.0048 \| 25.3977 \| 35.9471 \| 35.9807 \| 2.4804 \| 18.8182 \|
	\| 1.046 \| 14.1176 \| 1200 \| 0.9918 \| 32.6697 \| 21.3968 \| 30.8639 \| 31.0221 \| 2.4669 \| 18.7727 \|
	\| 1.046 \| 14.7059 \| 1250 \| 1.0598 \| 37.7878 \| 20.6971 \| 36.6794 \| 36.7289 \| 2.5767 \| 18.7727 \|
	\| 1.046 \| 15.2941 \| 1300 \| 1.0130 \| 34.549 \| 24.4177 \| 34.0376 \| 34.1226 \| 2.1773 \| 18.8182 \|
	\| 1.046 \| 15.8824 \| 1350 \| 1.0256 \| 32.774 \| 19.6047 \| 31.6125 \| 31.9067 \| 2.0504 \| 18.7727 \|
	\| 1.046 \| 16.4706 \| 1400 \| 1.0232 \| 31.4885 \| 18.4703 \| 30.0937 \| 30.5529 \| 2.514 \| 18.8182 \|
	\| 1.046 \| 17.0588 \| 1450 \| 1.0210 \| 33.4684 \| 20.7982 \| 31.7789 \| 32.0023 \| 2.4881 \| 18.7273 \|
	\| 0.7674 \| 17.6471 \| 1500 \| 1.0419 \| 37.4914 \| 20.9444 \| 35.0519 \| 35.2368 \| 3.0058 \| 18.7727 \|
	\| 0.7674 \| 18.2353 \| 1550 \| 1.0328 \| 36.5606 \| 21.0215 \| 35.2548 \| 35.4748 \| 2.7878 \| 18.7273 \|
	\| 0.7674 \| 18.8235 \| 1600 \| 1.0376 \| 31.3516 \| 18.5826 \| 29.6759 \| 29.8435 \| 2.3192 \| 18.8182 \|
	\| 0.7674 \| 19.4118 \| 1650 \| 1.0414 \| 37.4725 \| 22.3216 \| 35.6306 \| 35.7383 \| 2.477 \| 18.8182 \|
	\| 0.7674 \| 20.0 \| 1700 \| 1.0513 \| 39.5759 \| 23.2665 \| 39.2332 \| 39.3667 \| 2.4322 \| 18.7273 \|
	\| 0.7674 \| 20.5882 \| 1750 \| 1.0518 \| 36.1526 \| 23.8263 \| 34.5677 \| 34.6173 \| 2.8518 \| 18.7727 \|
	\| 0.7674 \| 21.1765 \| 1800 \| 1.0446 \| 41.5192 \| 23.3064 \| 39.3799 \| 39.6548 \| 3.0326 \| 18.8182 \|
	\| 0.7674 \| 21.7647 \| 1850 \| 1.0150 \| 40.5093 \| 21.8683 \| 38.2773 \| 38.6063 \| 2.6653 \| 18.8636 \|
	\| 0.7674 \| 22.3529 \| 1900 \| 1.0364 \| 34.2216 \| 20.2095 \| 32.5945 \| 32.6999 \| 2.6078 \| 18.8182 \|
	\| 0.7674 \| 22.9412 \| 1950 \| 1.0148 \| 39.8173 \| 20.6247 \| 37.2954 \| 37.6752 \| 3.0336 \| 18.8636 \|
	\| 0.6485 \| 23.5294 \| 2000 \| 1.0429 \| 40.2889 \| 21.1598 \| 37.7657 \| 38.0596 \| 2.9108 \| 18.8182 \|
	\| 0.6485 \| 24.1176 \| 2050 \| 1.0423 \| 39.2679 \| 20.8842 \| 36.7395 \| 36.9295 \| 2.845 \| 18.8636 \|
	\| 0.6485 \| 24.7059 \| 2100 \| 1.0358 \| 39.086 \| 20.7799 \| 36.2138 \| 36.3741 \| 2.9429 \| 18.8182 \|
	\| 0.6485 \| 25.2941 \| 2150 \| 1.0219 \| 38.754 \| 22.4097 \| 36.9752 \| 37.121 \| 2.831 \| 18.8182 \|
	\| 0.6485 \| 25.8824 \| 2200 \| 1.0450 \| 38.3531 \| 22.3593 \| 36.4439 \| 36.6304 \| 2.9804 \| 18.7727 \|
	\| 0.6485 \| 26.4706 \| 2250 \| 1.0482 \| 40.6921 \| 23.617 \| 39.298 \| 39.5895 \| 3.0971 \| 18.7727 \|
	\| 0.6485 \| 27.0588 \| 2300 \| 1.0495 \| 39.6761 \| 22.7969 \| 37.0805 \| 37.4949 \| 3.2639 \| 18.7727 \|
	\| 0.6485 \| 27.6471 \| 2350 \| 1.0412 \| 40.8199 \| 23.7109 \| 38.9222 \| 39.2493 \| 3.0267 \| 18.7273 \|
	\| 0.6485 \| 28.2353 \| 2400 \| 1.0453 \| 39.9504 \| 23.888 \| 38.0725 \| 38.3121 \| 3.2191 \| 18.7727 \|
	\| 0.6485 \| 28.8235 \| 2450 \| 1.0400 \| 36.205 \| 23.1356 \| 34.6087 \| 34.6263 \| 3.028 \| 18.7727 \|
	\| 0.5501 \| 29.4118 \| 2500 \| 1.0402 \| 35.033 \| 22.2393 \| 33.3754 \| 33.4477 \| 3.0299 \| 18.7273 \|
	\| 0.5501 \| 30.0 \| 2550 \| 1.0396 \| 35.5496 \| 22.9927 \| 33.7986 \| 33.9427 \| 3.0002 \| 18.7273 \|


	### Framework versions

	- Transformers 4.40.0
	- Pytorch 2.2.2
	- Datasets 2.12.0
	- Tokenizers 0.19.1