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predict-perception-bert-focus-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-focus-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-focus-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: 0.2271
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+ - Rmse: 0.5965
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+ - Rmse Focus::a Su un oggetto: 0.5965
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+ - Mae: 0.4372
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+ - Mae Focus::a Su un oggetto: 0.4372
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+ - R2: 0.4957
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+ - R2 Focus::a Su un oggetto: 0.4957
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+ - Cos: 0.6522
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+ - Pair: 0.0
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+ - Rank: 0.5
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+ - Neighbors: 0.6622
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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 Focus::a Su un oggetto | Mae | Mae Focus::a Su un oggetto | R2 | R2 Focus::a Su un oggetto | Cos | Pair | Rank | Neighbors | Rsa |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:------:|:-------------------------:|:------:|:----:|:----:|:---------:|:---:|
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+ | 1.0371 | 1.0 | 15 | 0.4358 | 0.8263 | 0.8263 | 0.7132 | 0.7132 | 0.0323 | 0.0323 | 0.3043 | 0.0 | 0.5 | 0.3510 | nan |
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+ | 0.9574 | 2.0 | 30 | 0.4420 | 0.8321 | 0.8321 | 0.7175 | 0.7175 | 0.0186 | 0.0186 | 0.3043 | 0.0 | 0.5 | 0.4627 | nan |
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+ | 0.9137 | 3.0 | 45 | 0.4208 | 0.8119 | 0.8119 | 0.6955 | 0.6955 | 0.0657 | 0.0657 | 0.3913 | 0.0 | 0.5 | 0.3928 | nan |
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+ | 0.8465 | 4.0 | 60 | 0.3356 | 0.7251 | 0.7251 | 0.6237 | 0.6237 | 0.2548 | 0.2548 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
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+ | 0.6864 | 5.0 | 75 | 0.2876 | 0.6712 | 0.6712 | 0.5624 | 0.5624 | 0.3616 | 0.3616 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
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+ | 0.5804 | 6.0 | 90 | 0.3148 | 0.7022 | 0.7022 | 0.5577 | 0.5577 | 0.3011 | 0.3011 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
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+ | 0.4983 | 7.0 | 105 | 0.4068 | 0.7983 | 0.7983 | 0.6606 | 0.6606 | 0.0968 | 0.0968 | 0.3913 | 0.0 | 0.5 | 0.4519 | nan |
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+ | 0.3584 | 8.0 | 120 | 0.2567 | 0.6342 | 0.6342 | 0.4883 | 0.4883 | 0.4300 | 0.4300 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
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+ | 0.2771 | 9.0 | 135 | 0.2130 | 0.5777 | 0.5777 | 0.4193 | 0.4193 | 0.5270 | 0.5270 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.2135 | 10.0 | 150 | 0.2522 | 0.6285 | 0.6285 | 0.4572 | 0.4572 | 0.4401 | 0.4401 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.1654 | 11.0 | 165 | 0.2662 | 0.6457 | 0.6457 | 0.4603 | 0.4603 | 0.4090 | 0.4090 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.1554 | 12.0 | 180 | 0.2459 | 0.6207 | 0.6207 | 0.4778 | 0.4778 | 0.4540 | 0.4540 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.1195 | 13.0 | 195 | 0.2385 | 0.6113 | 0.6113 | 0.4618 | 0.4618 | 0.4704 | 0.4704 | 0.5652 | 0.0 | 0.5 | 0.5693 | nan |
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+ | 0.1046 | 14.0 | 210 | 0.2296 | 0.5997 | 0.5997 | 0.4544 | 0.4544 | 0.4903 | 0.4903 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.089 | 15.0 | 225 | 0.2520 | 0.6283 | 0.6283 | 0.4974 | 0.4974 | 0.4404 | 0.4404 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.083 | 16.0 | 240 | 0.2297 | 0.5998 | 0.5998 | 0.4635 | 0.4635 | 0.4901 | 0.4901 | 0.5652 | 0.0 | 0.5 | 0.5610 | nan |
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+ | 0.0701 | 17.0 | 255 | 0.2207 | 0.5879 | 0.5879 | 0.4442 | 0.4442 | 0.5101 | 0.5101 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.0585 | 18.0 | 270 | 0.2397 | 0.6128 | 0.6128 | 0.4617 | 0.4617 | 0.4678 | 0.4678 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.0652 | 19.0 | 285 | 0.2284 | 0.5981 | 0.5981 | 0.4449 | 0.4449 | 0.4929 | 0.4929 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.059 | 20.0 | 300 | 0.2491 | 0.6247 | 0.6247 | 0.4599 | 0.4599 | 0.4469 | 0.4469 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.0464 | 21.0 | 315 | 0.2306 | 0.6010 | 0.6010 | 0.4373 | 0.4373 | 0.4880 | 0.4880 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.0529 | 22.0 | 330 | 0.2370 | 0.6093 | 0.6093 | 0.4480 | 0.4480 | 0.4738 | 0.4738 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.0555 | 23.0 | 345 | 0.2361 | 0.6082 | 0.6082 | 0.4474 | 0.4474 | 0.4757 | 0.4757 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.0447 | 24.0 | 360 | 0.2283 | 0.5980 | 0.5980 | 0.4399 | 0.4399 | 0.4932 | 0.4932 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.046 | 25.0 | 375 | 0.2259 | 0.5948 | 0.5948 | 0.4413 | 0.4413 | 0.4985 | 0.4985 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.0379 | 26.0 | 390 | 0.2263 | 0.5953 | 0.5953 | 0.4402 | 0.4402 | 0.4977 | 0.4977 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.0438 | 27.0 | 405 | 0.2270 | 0.5963 | 0.5963 | 0.4378 | 0.4378 | 0.4961 | 0.4961 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.0354 | 28.0 | 420 | 0.2211 | 0.5886 | 0.5886 | 0.4379 | 0.4379 | 0.5090 | 0.5090 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.0363 | 29.0 | 435 | 0.2269 | 0.5962 | 0.5962 | 0.4362 | 0.4362 | 0.4961 | 0.4961 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.0451 | 30.0 | 450 | 0.2271 | 0.5965 | 0.5965 | 0.4372 | 0.4372 | 0.4957 | 0.4957 | 0.6522 | 0.0 | 0.5 | 0.6622 | 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