Anwaarma/robertuito-esp

8838ab3 verified about 1 year ago

7.3 kB

	---
	library_name: transformers
	base_model: pysentimiento/robertuito-base-uncased
	tags:
	- generated_from_trainer
	metrics:
	- f1
	model-index:
	- name: robertuito-esp
	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. -->

	# robertuito-esp

	This model is a fine-tuned version of [pysentimiento/robertuito-base-uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- F1: 0.8528
	- Loss: 0.5317

	## 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: 2.728093668459819e-05
	- train_batch_size: 4
	- eval_batch_size: 4
	- seed: 42
	- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: linear
	- num_epochs: 3
	- mixed_precision_training: Native AMP
	- label_smoothing_factor: 0.1

	### Training results

	\| Training Loss \| Epoch \| Step \| F1 \| Validation Loss \|
	\|:-------------:\|:------:\|:----:\|:------:\|:---------------:\|
	\| 0.6839 \| 0.0313 \| 50 \| 0.7302 \| 0.5988 \|
	\| 0.5925 \| 0.0626 \| 100 \| 0.7898 \| 0.4998 \|
	\| 0.5793 \| 0.0939 \| 150 \| 0.8199 \| 0.4645 \|
	\| 0.4964 \| 0.1252 \| 200 \| 0.7882 \| 0.5048 \|
	\| 0.4979 \| 0.1565 \| 250 \| 0.8483 \| 0.4355 \|
	\| 0.5462 \| 0.1879 \| 300 \| 0.8589 \| 0.4264 \|
	\| 0.4271 \| 0.2192 \| 350 \| 0.8325 \| 0.4819 \|
	\| 0.4801 \| 0.2505 \| 400 \| 0.8688 \| 0.4230 \|
	\| 0.4246 \| 0.2818 \| 450 \| 0.8731 \| 0.4355 \|
	\| 0.4435 \| 0.3131 \| 500 \| 0.8730 \| 0.4197 \|
	\| 0.3257 \| 0.3444 \| 550 \| 0.8710 \| 0.4488 \|
	\| 0.4379 \| 0.3757 \| 600 \| 0.8652 \| 0.4428 \|
	\| 0.4813 \| 0.4070 \| 650 \| 0.8647 \| 0.4094 \|
	\| 0.49 \| 0.4383 \| 700 \| 0.8263 \| 0.4768 \|
	\| 0.3999 \| 0.4696 \| 750 \| 0.8467 \| 0.4463 \|
	\| 0.3629 \| 0.5009 \| 800 \| 0.8523 \| 0.4403 \|
	\| 0.403 \| 0.5322 \| 850 \| 0.8670 \| 0.4416 \|
	\| 0.3329 \| 0.5636 \| 900 \| 0.8547 \| 0.4821 \|
	\| 0.4652 \| 0.5949 \| 950 \| 0.8509 \| 0.4877 \|
	\| 0.4348 \| 0.6262 \| 1000 \| 0.8565 \| 0.4801 \|
	\| 0.3317 \| 0.6575 \| 1050 \| 0.8423 \| 0.4966 \|
	\| 0.46 \| 0.6888 \| 1100 \| 0.8327 \| 0.5275 \|
	\| 0.3597 \| 0.7201 \| 1150 \| 0.8508 \| 0.5103 \|
	\| 0.358 \| 0.7514 \| 1200 \| 0.8522 \| 0.4784 \|
	\| 0.3856 \| 0.7827 \| 1250 \| 0.8693 \| 0.4824 \|
	\| 0.3635 \| 0.8140 \| 1300 \| 0.8729 \| 0.4662 \|
	\| 0.4228 \| 0.8453 \| 1350 \| 0.8612 \| 0.4617 \|
	\| 0.3565 \| 0.8766 \| 1400 \| 0.8628 \| 0.4627 \|
	\| 0.3035 \| 0.9080 \| 1450 \| 0.8672 \| 0.4734 \|
	\| 0.407 \| 0.9393 \| 1500 \| 0.8641 \| 0.4566 \|
	\| 0.3273 \| 0.9706 \| 1550 \| 0.8531 \| 0.4912 \|
	\| 0.2871 \| 1.0019 \| 1600 \| 0.8673 \| 0.4843 \|
	\| 0.2829 \| 1.0332 \| 1650 \| 0.8591 \| 0.4843 \|
	\| 0.2512 \| 1.0645 \| 1700 \| 0.8588 \| 0.5057 \|
	\| 0.2945 \| 1.0958 \| 1750 \| 0.8448 \| 0.5404 \|
	\| 0.3107 \| 1.1271 \| 1800 \| 0.8647 \| 0.4773 \|
	\| 0.2441 \| 1.1584 \| 1850 \| 0.8530 \| 0.5198 \|
	\| 0.2744 \| 1.1897 \| 1900 \| 0.8669 \| 0.5051 \|
	\| 0.2469 \| 1.2210 \| 1950 \| 0.8569 \| 0.5106 \|
	\| 0.2532 \| 1.2523 \| 2000 \| 0.8692 \| 0.5018 \|
	\| 0.2995 \| 1.2837 \| 2050 \| 0.8651 \| 0.5020 \|
	\| 0.2461 \| 1.3150 \| 2100 \| 0.8571 \| 0.5256 \|
	\| 0.2463 \| 1.3463 \| 2150 \| 0.8653 \| 0.5064 \|
	\| 0.257 \| 1.3776 \| 2200 \| 0.8669 \| 0.4898 \|
	\| 0.2294 \| 1.4089 \| 2250 \| 0.8673 \| 0.4992 \|
	\| 0.2621 \| 1.4402 \| 2300 \| 0.8652 \| 0.5104 \|
	\| 0.2373 \| 1.4715 \| 2350 \| 0.8487 \| 0.5130 \|
	\| 0.2367 \| 1.5028 \| 2400 \| 0.8448 \| 0.5559 \|
	\| 0.2464 \| 1.5341 \| 2450 \| 0.8653 \| 0.5204 \|
	\| 0.2348 \| 1.5654 \| 2500 \| 0.8693 \| 0.5159 \|
	\| 0.2069 \| 1.5967 \| 2550 \| 0.8588 \| 0.5004 \|
	\| 0.2213 \| 1.6281 \| 2600 \| 0.8592 \| 0.5359 \|
	\| 0.2264 \| 1.6594 \| 2650 \| 0.8652 \| 0.5244 \|
	\| 0.2296 \| 1.6907 \| 2700 \| 0.8611 \| 0.5211 \|
	\| 0.2366 \| 1.7220 \| 2750 \| 0.8592 \| 0.5117 \|
	\| 0.2392 \| 1.7533 \| 2800 \| 0.8706 \| 0.4882 \|
	\| 0.2636 \| 1.7846 \| 2850 \| 0.8713 \| 0.4988 \|
	\| 0.2426 \| 1.8159 \| 2900 \| 0.8732 \| 0.4955 \|
	\| 0.2541 \| 1.8472 \| 2950 \| 0.8690 \| 0.4957 \|
	\| 0.2625 \| 1.8785 \| 3000 \| 0.8752 \| 0.4843 \|
	\| 0.2151 \| 1.9098 \| 3050 \| 0.8710 \| 0.5104 \|
	\| 0.2214 \| 1.9411 \| 3100 \| 0.8710 \| 0.5103 \|
	\| 0.2708 \| 1.9724 \| 3150 \| 0.8639 \| 0.4959 \|
	\| 0.2593 \| 2.0038 \| 3200 \| 0.8652 \| 0.5207 \|
	\| 0.2233 \| 2.0351 \| 3250 \| 0.8611 \| 0.5260 \|
	\| 0.2223 \| 2.0664 \| 3300 \| 0.8671 \| 0.5186 \|
	\| 0.2262 \| 2.0977 \| 3350 \| 0.8705 \| 0.4925 \|
	\| 0.2297 \| 2.1290 \| 3400 \| 0.8610 \| 0.5214 \|
	\| 0.2042 \| 2.1603 \| 3450 \| 0.8590 \| 0.5329 \|
	\| 0.2238 \| 2.1916 \| 3500 \| 0.8489 \| 0.5318 \|
	\| 0.2109 \| 2.2229 \| 3550 \| 0.8570 \| 0.5286 \|
	\| 0.226 \| 2.2542 \| 3600 \| 0.8630 \| 0.5232 \|
	\| 0.2594 \| 2.2855 \| 3650 \| 0.8651 \| 0.5132 \|
	\| 0.2264 \| 2.3168 \| 3700 \| 0.8570 \| 0.5239 \|
	\| 0.2025 \| 2.3482 \| 3750 \| 0.8590 \| 0.5274 \|
	\| 0.2064 \| 2.3795 \| 3800 \| 0.8609 \| 0.5045 \|
	\| 0.2004 \| 2.4108 \| 3850 \| 0.8650 \| 0.5114 \|
	\| 0.2278 \| 2.4421 \| 3900 \| 0.8489 \| 0.5277 \|
	\| 0.2193 \| 2.4734 \| 3950 \| 0.8610 \| 0.5227 \|
	\| 0.2231 \| 2.5047 \| 4000 \| 0.8609 \| 0.5109 \|
	\| 0.207 \| 2.5360 \| 4050 \| 0.8566 \| 0.5087 \|
	\| 0.1995 \| 2.5673 \| 4100 \| 0.8630 \| 0.5221 \|
	\| 0.2125 \| 2.5986 \| 4150 \| 0.8610 \| 0.5242 \|
	\| 0.2014 \| 2.6299 \| 4200 \| 0.8550 \| 0.5371 \|
	\| 0.2118 \| 2.6612 \| 4250 \| 0.8591 \| 0.5321 \|
	\| 0.1995 \| 2.6925 \| 4300 \| 0.8550 \| 0.5375 \|
	\| 0.2258 \| 2.7239 \| 4350 \| 0.8550 \| 0.5352 \|
	\| 0.1994 \| 2.7552 \| 4400 \| 0.8570 \| 0.5390 \|
	\| 0.2235 \| 2.7865 \| 4450 \| 0.8570 \| 0.5306 \|
	\| 0.2109 \| 2.8178 \| 4500 \| 0.8530 \| 0.5452 \|
	\| 0.2091 \| 2.8491 \| 4550 \| 0.8550 \| 0.5345 \|
	\| 0.1994 \| 2.8804 \| 4600 \| 0.8550 \| 0.5356 \|
	\| 0.2134 \| 2.9117 \| 4650 \| 0.8529 \| 0.5368 \|
	\| 0.2111 \| 2.9430 \| 4700 \| 0.8508 \| 0.5317 \|
	\| 0.1995 \| 2.9743 \| 4750 \| 0.8528 \| 0.5317 \|


	### Framework versions

	- Transformers 4.51.3
	- Pytorch 2.7.0+cu126
	- Datasets 3.5.0
	- Tokenizers 0.21.1

	---
	library_name: transformers
	base_model: pysentimiento/robertuito-base-uncased
	tags:
	- generated_from_trainer
	metrics:
	- f1
	model-index:
	- name: robertuito-esp
	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. -->

	# robertuito-esp

	This model is a fine-tuned version of [pysentimiento/robertuito-base-uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- F1: 0.8528
	- Loss: 0.5317

	## 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: 2.728093668459819e-05
	- train_batch_size: 4
	- eval_batch_size: 4
	- seed: 42
	- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: linear
	- num_epochs: 3
	- mixed_precision_training: Native AMP
	- label_smoothing_factor: 0.1

	### Training results

	\| Training Loss \| Epoch \| Step \| F1 \| Validation Loss \|
	\|:-------------:\|:------:\|:----:\|:------:\|:---------------:\|
	\| 0.6839 \| 0.0313 \| 50 \| 0.7302 \| 0.5988 \|
	\| 0.5925 \| 0.0626 \| 100 \| 0.7898 \| 0.4998 \|
	\| 0.5793 \| 0.0939 \| 150 \| 0.8199 \| 0.4645 \|
	\| 0.4964 \| 0.1252 \| 200 \| 0.7882 \| 0.5048 \|
	\| 0.4979 \| 0.1565 \| 250 \| 0.8483 \| 0.4355 \|
	\| 0.5462 \| 0.1879 \| 300 \| 0.8589 \| 0.4264 \|
	\| 0.4271 \| 0.2192 \| 350 \| 0.8325 \| 0.4819 \|
	\| 0.4801 \| 0.2505 \| 400 \| 0.8688 \| 0.4230 \|
	\| 0.4246 \| 0.2818 \| 450 \| 0.8731 \| 0.4355 \|
	\| 0.4435 \| 0.3131 \| 500 \| 0.8730 \| 0.4197 \|
	\| 0.3257 \| 0.3444 \| 550 \| 0.8710 \| 0.4488 \|
	\| 0.4379 \| 0.3757 \| 600 \| 0.8652 \| 0.4428 \|
	\| 0.4813 \| 0.4070 \| 650 \| 0.8647 \| 0.4094 \|
	\| 0.49 \| 0.4383 \| 700 \| 0.8263 \| 0.4768 \|
	\| 0.3999 \| 0.4696 \| 750 \| 0.8467 \| 0.4463 \|
	\| 0.3629 \| 0.5009 \| 800 \| 0.8523 \| 0.4403 \|
	\| 0.403 \| 0.5322 \| 850 \| 0.8670 \| 0.4416 \|
	\| 0.3329 \| 0.5636 \| 900 \| 0.8547 \| 0.4821 \|
	\| 0.4652 \| 0.5949 \| 950 \| 0.8509 \| 0.4877 \|
	\| 0.4348 \| 0.6262 \| 1000 \| 0.8565 \| 0.4801 \|
	\| 0.3317 \| 0.6575 \| 1050 \| 0.8423 \| 0.4966 \|
	\| 0.46 \| 0.6888 \| 1100 \| 0.8327 \| 0.5275 \|
	\| 0.3597 \| 0.7201 \| 1150 \| 0.8508 \| 0.5103 \|
	\| 0.358 \| 0.7514 \| 1200 \| 0.8522 \| 0.4784 \|
	\| 0.3856 \| 0.7827 \| 1250 \| 0.8693 \| 0.4824 \|
	\| 0.3635 \| 0.8140 \| 1300 \| 0.8729 \| 0.4662 \|
	\| 0.4228 \| 0.8453 \| 1350 \| 0.8612 \| 0.4617 \|
	\| 0.3565 \| 0.8766 \| 1400 \| 0.8628 \| 0.4627 \|
	\| 0.3035 \| 0.9080 \| 1450 \| 0.8672 \| 0.4734 \|
	\| 0.407 \| 0.9393 \| 1500 \| 0.8641 \| 0.4566 \|
	\| 0.3273 \| 0.9706 \| 1550 \| 0.8531 \| 0.4912 \|
	\| 0.2871 \| 1.0019 \| 1600 \| 0.8673 \| 0.4843 \|
	\| 0.2829 \| 1.0332 \| 1650 \| 0.8591 \| 0.4843 \|
	\| 0.2512 \| 1.0645 \| 1700 \| 0.8588 \| 0.5057 \|
	\| 0.2945 \| 1.0958 \| 1750 \| 0.8448 \| 0.5404 \|
	\| 0.3107 \| 1.1271 \| 1800 \| 0.8647 \| 0.4773 \|
	\| 0.2441 \| 1.1584 \| 1850 \| 0.8530 \| 0.5198 \|
	\| 0.2744 \| 1.1897 \| 1900 \| 0.8669 \| 0.5051 \|
	\| 0.2469 \| 1.2210 \| 1950 \| 0.8569 \| 0.5106 \|
	\| 0.2532 \| 1.2523 \| 2000 \| 0.8692 \| 0.5018 \|
	\| 0.2995 \| 1.2837 \| 2050 \| 0.8651 \| 0.5020 \|
	\| 0.2461 \| 1.3150 \| 2100 \| 0.8571 \| 0.5256 \|
	\| 0.2463 \| 1.3463 \| 2150 \| 0.8653 \| 0.5064 \|
	\| 0.257 \| 1.3776 \| 2200 \| 0.8669 \| 0.4898 \|
	\| 0.2294 \| 1.4089 \| 2250 \| 0.8673 \| 0.4992 \|
	\| 0.2621 \| 1.4402 \| 2300 \| 0.8652 \| 0.5104 \|
	\| 0.2373 \| 1.4715 \| 2350 \| 0.8487 \| 0.5130 \|
	\| 0.2367 \| 1.5028 \| 2400 \| 0.8448 \| 0.5559 \|
	\| 0.2464 \| 1.5341 \| 2450 \| 0.8653 \| 0.5204 \|
	\| 0.2348 \| 1.5654 \| 2500 \| 0.8693 \| 0.5159 \|
	\| 0.2069 \| 1.5967 \| 2550 \| 0.8588 \| 0.5004 \|
	\| 0.2213 \| 1.6281 \| 2600 \| 0.8592 \| 0.5359 \|
	\| 0.2264 \| 1.6594 \| 2650 \| 0.8652 \| 0.5244 \|
	\| 0.2296 \| 1.6907 \| 2700 \| 0.8611 \| 0.5211 \|
	\| 0.2366 \| 1.7220 \| 2750 \| 0.8592 \| 0.5117 \|
	\| 0.2392 \| 1.7533 \| 2800 \| 0.8706 \| 0.4882 \|
	\| 0.2636 \| 1.7846 \| 2850 \| 0.8713 \| 0.4988 \|
	\| 0.2426 \| 1.8159 \| 2900 \| 0.8732 \| 0.4955 \|
	\| 0.2541 \| 1.8472 \| 2950 \| 0.8690 \| 0.4957 \|
	\| 0.2625 \| 1.8785 \| 3000 \| 0.8752 \| 0.4843 \|
	\| 0.2151 \| 1.9098 \| 3050 \| 0.8710 \| 0.5104 \|
	\| 0.2214 \| 1.9411 \| 3100 \| 0.8710 \| 0.5103 \|
	\| 0.2708 \| 1.9724 \| 3150 \| 0.8639 \| 0.4959 \|
	\| 0.2593 \| 2.0038 \| 3200 \| 0.8652 \| 0.5207 \|
	\| 0.2233 \| 2.0351 \| 3250 \| 0.8611 \| 0.5260 \|
	\| 0.2223 \| 2.0664 \| 3300 \| 0.8671 \| 0.5186 \|
	\| 0.2262 \| 2.0977 \| 3350 \| 0.8705 \| 0.4925 \|
	\| 0.2297 \| 2.1290 \| 3400 \| 0.8610 \| 0.5214 \|
	\| 0.2042 \| 2.1603 \| 3450 \| 0.8590 \| 0.5329 \|
	\| 0.2238 \| 2.1916 \| 3500 \| 0.8489 \| 0.5318 \|
	\| 0.2109 \| 2.2229 \| 3550 \| 0.8570 \| 0.5286 \|
	\| 0.226 \| 2.2542 \| 3600 \| 0.8630 \| 0.5232 \|
	\| 0.2594 \| 2.2855 \| 3650 \| 0.8651 \| 0.5132 \|
	\| 0.2264 \| 2.3168 \| 3700 \| 0.8570 \| 0.5239 \|
	\| 0.2025 \| 2.3482 \| 3750 \| 0.8590 \| 0.5274 \|
	\| 0.2064 \| 2.3795 \| 3800 \| 0.8609 \| 0.5045 \|
	\| 0.2004 \| 2.4108 \| 3850 \| 0.8650 \| 0.5114 \|
	\| 0.2278 \| 2.4421 \| 3900 \| 0.8489 \| 0.5277 \|
	\| 0.2193 \| 2.4734 \| 3950 \| 0.8610 \| 0.5227 \|
	\| 0.2231 \| 2.5047 \| 4000 \| 0.8609 \| 0.5109 \|
	\| 0.207 \| 2.5360 \| 4050 \| 0.8566 \| 0.5087 \|
	\| 0.1995 \| 2.5673 \| 4100 \| 0.8630 \| 0.5221 \|
	\| 0.2125 \| 2.5986 \| 4150 \| 0.8610 \| 0.5242 \|
	\| 0.2014 \| 2.6299 \| 4200 \| 0.8550 \| 0.5371 \|
	\| 0.2118 \| 2.6612 \| 4250 \| 0.8591 \| 0.5321 \|
	\| 0.1995 \| 2.6925 \| 4300 \| 0.8550 \| 0.5375 \|
	\| 0.2258 \| 2.7239 \| 4350 \| 0.8550 \| 0.5352 \|
	\| 0.1994 \| 2.7552 \| 4400 \| 0.8570 \| 0.5390 \|
	\| 0.2235 \| 2.7865 \| 4450 \| 0.8570 \| 0.5306 \|
	\| 0.2109 \| 2.8178 \| 4500 \| 0.8530 \| 0.5452 \|
	\| 0.2091 \| 2.8491 \| 4550 \| 0.8550 \| 0.5345 \|
	\| 0.1994 \| 2.8804 \| 4600 \| 0.8550 \| 0.5356 \|
	\| 0.2134 \| 2.9117 \| 4650 \| 0.8529 \| 0.5368 \|
	\| 0.2111 \| 2.9430 \| 4700 \| 0.8508 \| 0.5317 \|
	\| 0.1995 \| 2.9743 \| 4750 \| 0.8528 \| 0.5317 \|


	### Framework versions

	- Transformers 4.51.3
	- Pytorch 2.7.0+cu126
	- Datasets 3.5.0
	- Tokenizers 0.21.1