Gosse Minnema

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	---
	license: mit
	tags:
	- generated_from_trainer
	model-index:
	- name: predict-perception-bert-blame-object
	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. -->

	# predict-perception-bert-blame-object

	This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.5837
	- Rmse: 0.5589
	- Rmse Blame::a Un oggetto: 0.5589
	- Mae: 0.3862
	- Mae Blame::a Un oggetto: 0.3862
	- R2: 0.2884
	- R2 Blame::a Un oggetto: 0.2884
	- Cos: 0.3913
	- Pair: 0.0
	- Rank: 0.5
	- Neighbors: 0.5024
	- Rsa: nan

	## 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: 1e-05
	- train_batch_size: 20
	- eval_batch_size: 8
	- seed: 1996
	- 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 \| Rmse \| Rmse Blame::a Un oggetto \| Mae \| Mae Blame::a Un oggetto \| R2 \| R2 Blame::a Un oggetto \| Cos \| Pair \| Rank \| Neighbors \| Rsa \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------------------------:\|:------:\|:-----------------------:\|:-------:\|:----------------------:\|:------:\|:----:\|:----:\|:---------:\|:---:\|
	\| 1.0603 \| 1.0 \| 15 \| 0.8503 \| 0.6745 \| 0.6745 \| 0.4386 \| 0.4386 \| -0.0365 \| -0.0365 \| 0.1304 \| 0.0 \| 0.5 \| 0.5197 \| nan \|
	\| 0.9662 \| 2.0 \| 30 \| 0.8510 \| 0.6748 \| 0.6748 \| 0.4548 \| 0.4548 \| -0.0374 \| -0.0374 \| 0.0435 \| 0.0 \| 0.5 \| 0.4840 \| nan \|
	\| 0.9438 \| 3.0 \| 45 \| 0.7622 \| 0.6386 \| 0.6386 \| 0.4541 \| 0.4541 \| 0.0709 \| 0.0709 \| 0.0435 \| 0.0 \| 0.5 \| 0.4635 \| nan \|
	\| 0.9096 \| 4.0 \| 60 \| 0.8301 \| 0.6665 \| 0.6665 \| 0.4305 \| 0.4305 \| -0.0119 \| -0.0119 \| 0.0435 \| 0.0 \| 0.5 \| 0.3499 \| nan \|
	\| 0.8383 \| 5.0 \| 75 \| 0.7306 \| 0.6252 \| 0.6252 \| 0.3814 \| 0.3814 \| 0.1094 \| 0.1094 \| 0.3043 \| 0.0 \| 0.5 \| 0.5098 \| nan \|
	\| 0.7828 \| 6.0 \| 90 \| 0.7434 \| 0.6307 \| 0.6307 \| 0.4005 \| 0.4005 \| 0.0937 \| 0.0937 \| 0.3043 \| 0.0 \| 0.5 \| 0.4335 \| nan \|
	\| 0.7028 \| 7.0 \| 105 \| 0.7218 \| 0.6214 \| 0.6214 \| 0.4090 \| 0.4090 \| 0.1202 \| 0.1202 \| 0.3913 \| 0.0 \| 0.5 \| 0.4470 \| nan \|
	\| 0.6661 \| 8.0 \| 120 \| 0.7434 \| 0.6307 \| 0.6307 \| 0.4042 \| 0.4042 \| 0.0938 \| 0.0938 \| 0.3913 \| 0.0 \| 0.5 \| 0.4470 \| nan \|
	\| 0.578 \| 9.0 \| 135 \| 0.7719 \| 0.6426 \| 0.6426 \| 0.3975 \| 0.3975 \| 0.0591 \| 0.0591 \| 0.3913 \| 0.0 \| 0.5 \| 0.4470 \| nan \|
	\| 0.544 \| 10.0 \| 150 \| 0.7117 \| 0.6171 \| 0.6171 \| 0.4126 \| 0.4126 \| 0.1324 \| 0.1324 \| 0.2174 \| 0.0 \| 0.5 \| 0.3489 \| nan \|
	\| 0.4638 \| 11.0 \| 165 \| 0.6683 \| 0.5980 \| 0.5980 \| 0.3952 \| 0.3952 \| 0.1853 \| 0.1853 \| 0.3043 \| 0.0 \| 0.5 \| 0.3989 \| nan \|
	\| 0.3998 \| 12.0 \| 180 \| 0.6772 \| 0.6019 \| 0.6019 \| 0.4201 \| 0.4201 \| 0.1745 \| 0.1745 \| 0.3043 \| 0.0 \| 0.5 \| 0.3989 \| nan \|
	\| 0.3403 \| 13.0 \| 195 \| 0.6576 \| 0.5932 \| 0.5932 \| 0.4237 \| 0.4237 \| 0.1984 \| 0.1984 \| 0.2174 \| 0.0 \| 0.5 \| 0.3491 \| nan \|
	\| 0.2839 \| 14.0 \| 210 \| 0.6281 \| 0.5797 \| 0.5797 \| 0.4208 \| 0.4208 \| 0.2344 \| 0.2344 \| 0.2174 \| 0.0 \| 0.5 \| 0.3491 \| nan \|
	\| 0.2619 \| 15.0 \| 225 \| 0.6254 \| 0.5785 \| 0.5785 \| 0.3752 \| 0.3752 \| 0.2376 \| 0.2376 \| 0.3913 \| 0.0 \| 0.5 \| 0.5756 \| nan \|
	\| 0.2175 \| 16.0 \| 240 \| 0.6074 \| 0.5701 \| 0.5701 \| 0.3985 \| 0.3985 \| 0.2596 \| 0.2596 \| 0.3043 \| 0.0 \| 0.5 \| 0.4142 \| nan \|
	\| 0.1884 \| 17.0 \| 255 \| 0.6045 \| 0.5687 \| 0.5687 \| 0.4036 \| 0.4036 \| 0.2631 \| 0.2631 \| 0.3913 \| 0.0 \| 0.5 \| 0.5024 \| nan \|
	\| 0.1797 \| 18.0 \| 270 \| 0.6038 \| 0.5684 \| 0.5684 \| 0.3914 \| 0.3914 \| 0.2640 \| 0.2640 \| 0.3913 \| 0.0 \| 0.5 \| 0.5024 \| nan \|
	\| 0.1316 \| 19.0 \| 285 \| 0.6199 \| 0.5759 \| 0.5759 \| 0.4078 \| 0.4078 \| 0.2443 \| 0.2443 \| 0.3913 \| 0.0 \| 0.5 \| 0.5024 \| nan \|
	\| 0.1429 \| 20.0 \| 300 \| 0.6119 \| 0.5722 \| 0.5722 \| 0.3954 \| 0.3954 \| 0.2540 \| 0.2540 \| 0.3913 \| 0.0 \| 0.5 \| 0.5024 \| nan \|
	\| 0.1202 \| 21.0 \| 315 \| 0.6193 \| 0.5756 \| 0.5756 \| 0.3987 \| 0.3987 \| 0.2451 \| 0.2451 \| 0.3913 \| 0.0 \| 0.5 \| 0.5024 \| nan \|
	\| 0.1159 \| 22.0 \| 330 \| 0.6218 \| 0.5768 \| 0.5768 \| 0.3995 \| 0.3995 \| 0.2420 \| 0.2420 \| 0.3913 \| 0.0 \| 0.5 \| 0.5024 \| nan \|
	\| 0.1027 \| 23.0 \| 345 \| 0.6207 \| 0.5763 \| 0.5763 \| 0.4100 \| 0.4100 \| 0.2433 \| 0.2433 \| 0.3043 \| 0.0 \| 0.5 \| 0.4142 \| nan \|
	\| 0.1006 \| 24.0 \| 360 \| 0.5646 \| 0.5496 \| 0.5496 \| 0.3687 \| 0.3687 \| 0.3117 \| 0.3117 \| 0.3913 \| 0.0 \| 0.5 \| 0.5024 \| nan \|
	\| 0.0902 \| 25.0 \| 375 \| 0.5582 \| 0.5465 \| 0.5465 \| 0.3714 \| 0.3714 \| 0.3196 \| 0.3196 \| 0.3913 \| 0.0 \| 0.5 \| 0.5024 \| nan \|
	\| 0.0901 \| 26.0 \| 390 \| 0.5650 \| 0.5498 \| 0.5498 \| 0.3704 \| 0.3704 \| 0.3112 \| 0.3112 \| 0.3913 \| 0.0 \| 0.5 \| 0.5024 \| nan \|
	\| 0.0937 \| 27.0 \| 405 \| 0.5713 \| 0.5529 \| 0.5529 \| 0.3735 \| 0.3735 \| 0.3036 \| 0.3036 \| 0.3913 \| 0.0 \| 0.5 \| 0.5024 \| nan \|
	\| 0.0812 \| 28.0 \| 420 \| 0.5773 \| 0.5558 \| 0.5558 \| 0.3759 \| 0.3759 \| 0.2962 \| 0.2962 \| 0.3913 \| 0.0 \| 0.5 \| 0.5024 \| nan \|
	\| 0.0911 \| 29.0 \| 435 \| 0.5818 \| 0.5579 \| 0.5579 \| 0.3832 \| 0.3832 \| 0.2908 \| 0.2908 \| 0.3913 \| 0.0 \| 0.5 \| 0.5024 \| nan \|
	\| 0.082 \| 30.0 \| 450 \| 0.5837 \| 0.5589 \| 0.5589 \| 0.3862 \| 0.3862 \| 0.2884 \| 0.2884 \| 0.3913 \| 0.0 \| 0.5 \| 0.5024 \| nan \|


	### Framework versions

	- Transformers 4.16.2
	- Pytorch 1.10.2+cu113
	- Datasets 1.18.3
	- Tokenizers 0.11.0