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predict-perception-xlmr-focus-victim

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xlm-roberta
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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-xlmr-focus-victim
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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-xlmr-focus-victim
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+
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+ This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2546
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+ - Rmse: 0.6301
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+ - Rmse Focus::a Sulla vittima: 0.6301
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+ - Mae: 0.5441
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+ - Mae Focus::a Sulla vittima: 0.5441
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+ - R2: 0.7205
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+ - R2 Focus::a Sulla vittima: 0.7205
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+ - Cos: 0.8261
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+ - Pair: 0.0
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+ - Rank: 0.5
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+ - Neighbors: 0.7802
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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 Sulla vittima | Mae | Mae Focus::a Sulla vittima | R2 | R2 Focus::a Sulla vittima | Cos | Pair | Rank | Neighbors | Rsa |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:-------:|:-------------------------:|:------:|:----:|:----:|:---------:|:---:|
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+ | 1.0607 | 1.0 | 15 | 0.9261 | 1.2017 | 1.2017 | 0.9557 | 0.9557 | -0.0166 | -0.0166 | 0.4783 | 0.0 | 0.5 | 0.6332 | nan |
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+ | 1.0107 | 2.0 | 30 | 0.9481 | 1.2159 | 1.2159 | 0.9861 | 0.9861 | -0.0408 | -0.0408 | 0.4783 | 0.0 | 0.5 | 0.6332 | nan |
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+ | 0.9921 | 3.0 | 45 | 0.9068 | 1.1892 | 1.1892 | 0.9548 | 0.9548 | 0.0045 | 0.0045 | 0.4783 | 0.0 | 0.5 | 0.6332 | nan |
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+ | 0.7769 | 4.0 | 60 | 0.5014 | 0.8842 | 0.8842 | 0.7121 | 0.7121 | 0.4496 | 0.4496 | 0.7391 | 0.0 | 0.5 | 0.6232 | nan |
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+ | 0.5763 | 5.0 | 75 | 0.4019 | 0.7917 | 0.7917 | 0.6737 | 0.6737 | 0.5588 | 0.5588 | 0.8261 | 0.0 | 0.5 | 0.8155 | nan |
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+ | 0.4378 | 6.0 | 90 | 0.3594 | 0.7486 | 0.7486 | 0.5957 | 0.5957 | 0.6055 | 0.6055 | 0.7391 | 0.0 | 0.5 | 0.4442 | nan |
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+ | 0.3595 | 7.0 | 105 | 0.3452 | 0.7337 | 0.7337 | 0.6333 | 0.6333 | 0.6210 | 0.6210 | 0.5652 | 0.0 | 0.5 | 0.2649 | nan |
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+ | 0.3192 | 8.0 | 120 | 0.3275 | 0.7147 | 0.7147 | 0.6205 | 0.6205 | 0.6405 | 0.6405 | 0.7391 | 0.0 | 0.5 | 0.6561 | nan |
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+ | 0.2482 | 9.0 | 135 | 0.2978 | 0.6815 | 0.6815 | 0.5754 | 0.5754 | 0.6731 | 0.6731 | 0.7391 | 0.0 | 0.5 | 0.6715 | nan |
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+ | 0.2416 | 10.0 | 150 | 0.3018 | 0.6860 | 0.6860 | 0.5954 | 0.5954 | 0.6687 | 0.6687 | 0.5652 | 0.0 | 0.5 | 0.2553 | nan |
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+ | 0.2292 | 11.0 | 165 | 0.2764 | 0.6565 | 0.6565 | 0.5522 | 0.5522 | 0.6966 | 0.6966 | 0.9130 | 0.0 | 0.5 | 0.8408 | nan |
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+ | 0.1752 | 12.0 | 180 | 0.3070 | 0.6920 | 0.6920 | 0.5680 | 0.5680 | 0.6629 | 0.6629 | 0.7391 | 0.0 | 0.5 | 0.6715 | nan |
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+ | 0.1956 | 13.0 | 195 | 0.2923 | 0.6752 | 0.6752 | 0.5499 | 0.5499 | 0.6791 | 0.6791 | 0.8261 | 0.0 | 0.5 | 0.7843 | nan |
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+ | 0.1424 | 14.0 | 210 | 0.3163 | 0.7023 | 0.7023 | 0.6060 | 0.6060 | 0.6528 | 0.6528 | 0.9130 | 0.0 | 0.5 | 0.8408 | nan |
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+ | 0.152 | 15.0 | 225 | 0.2436 | 0.6164 | 0.6164 | 0.5127 | 0.5127 | 0.7326 | 0.7326 | 0.9130 | 0.0 | 0.5 | 0.8408 | nan |
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+ | 0.1277 | 16.0 | 240 | 0.2471 | 0.6208 | 0.6208 | 0.5367 | 0.5367 | 0.7287 | 0.7287 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
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+ | 0.1269 | 17.0 | 255 | 0.2573 | 0.6334 | 0.6334 | 0.5329 | 0.5329 | 0.7175 | 0.7175 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
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+ | 0.1058 | 18.0 | 270 | 0.2538 | 0.6291 | 0.6291 | 0.5530 | 0.5530 | 0.7214 | 0.7214 | 0.7391 | 0.0 | 0.5 | 0.2347 | nan |
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+ | 0.107 | 19.0 | 285 | 0.2568 | 0.6328 | 0.6328 | 0.5464 | 0.5464 | 0.7181 | 0.7181 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
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+ | 0.1185 | 20.0 | 300 | 0.2452 | 0.6183 | 0.6183 | 0.5317 | 0.5317 | 0.7309 | 0.7309 | 0.7391 | 0.0 | 0.5 | 0.2347 | nan |
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+ | 0.1029 | 21.0 | 315 | 0.2419 | 0.6142 | 0.6142 | 0.5415 | 0.5415 | 0.7344 | 0.7344 | 0.7391 | 0.0 | 0.5 | 0.2347 | nan |
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+ | 0.0908 | 22.0 | 330 | 0.2462 | 0.6196 | 0.6196 | 0.5261 | 0.5261 | 0.7297 | 0.7297 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
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+ | 0.0901 | 23.0 | 345 | 0.2528 | 0.6279 | 0.6279 | 0.5330 | 0.5330 | 0.7225 | 0.7225 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
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+ | 0.0979 | 24.0 | 360 | 0.2800 | 0.6607 | 0.6607 | 0.5682 | 0.5682 | 0.6927 | 0.6927 | 0.9130 | 0.0 | 0.5 | 0.8408 | nan |
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+ | 0.0992 | 25.0 | 375 | 0.2502 | 0.6246 | 0.6246 | 0.5517 | 0.5517 | 0.7254 | 0.7254 | 0.6522 | 0.0 | 0.5 | 0.2372 | nan |
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+ | 0.0846 | 26.0 | 390 | 0.2570 | 0.6331 | 0.6331 | 0.5524 | 0.5524 | 0.7178 | 0.7178 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
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+ | 0.0717 | 27.0 | 405 | 0.2562 | 0.6321 | 0.6321 | 0.5456 | 0.5456 | 0.7187 | 0.7187 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
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+ | 0.0739 | 28.0 | 420 | 0.2570 | 0.6330 | 0.6330 | 0.5471 | 0.5471 | 0.7179 | 0.7179 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
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+ | 0.0828 | 29.0 | 435 | 0.2553 | 0.6309 | 0.6309 | 0.5446 | 0.5446 | 0.7198 | 0.7198 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
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+ | 0.086 | 30.0 | 450 | 0.2546 | 0.6301 | 0.6301 | 0.5441 | 0.5441 | 0.7205 | 0.7205 | 0.8261 | 0.0 | 0.5 | 0.7802 | 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