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predict-perception-bert-blame-object

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+ ---
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+ license: mit
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: predict-perception-bert-blame-object
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # predict-perception-bert-blame-object
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+
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+ 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.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5837
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+ - Rmse: 0.5589
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+ - Rmse Blame::a Un oggetto: 0.5589
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+ - Mae: 0.3862
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+ - Mae Blame::a Un oggetto: 0.3862
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+ - R2: 0.2884
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+ - R2 Blame::a Un oggetto: 0.2884
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+ - Cos: 0.3913
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+ - Pair: 0.0
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+ - Rank: 0.5
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+ - Neighbors: 0.5024
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+ - Rsa: nan
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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+ - train_batch_size: 20
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+ - eval_batch_size: 8
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+ - seed: 1996
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 30
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+
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+ ### Training results
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+
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+ | 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 |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------:|:------:|:-----------------------:|:-------:|:----------------------:|:------:|:----:|:----:|:---------:|:---:|
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.16.2
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+ - Pytorch 1.10.2+cu113
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+ - Datasets 1.18.3
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+ - Tokenizers 0.11.0