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predict-perception-xlmr-blame-assassin

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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-blame-assassin
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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-blame-assassin
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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: 0.4439
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+ - Rmse: 0.9571
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+ - Rmse Blame::a L'assassino: 0.9571
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+ - Mae: 0.7260
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+ - Mae Blame::a L'assassino: 0.7260
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+ - R2: 0.6437
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+ - R2 Blame::a L'assassino: 0.6437
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+ - Cos: 0.7391
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+ - Pair: 0.0
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+ - Rank: 0.5
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+ - Neighbors: 0.6287
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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 L'assassino | Mae | Mae Blame::a L'assassino | R2 | R2 Blame::a L'assassino | Cos | Pair | Rank | Neighbors | Rsa |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:-------------------------:|:------:|:------------------------:|:------:|:-----------------------:|:------:|:----:|:----:|:---------:|:---:|
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+ | 1.0317 | 1.0 | 15 | 1.1311 | 1.5278 | 1.5278 | 1.3893 | 1.3893 | 0.0919 | 0.0919 | 0.5652 | 0.0 | 0.5 | 0.4512 | nan |
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+ | 0.9475 | 2.0 | 30 | 1.0795 | 1.4926 | 1.4926 | 1.3387 | 1.3387 | 0.1334 | 0.1334 | 0.8261 | 0.0 | 0.5 | 0.6184 | nan |
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+ | 0.9146 | 3.0 | 45 | 1.1092 | 1.5130 | 1.5130 | 1.4078 | 1.4078 | 0.1095 | 0.1095 | 0.4783 | 0.0 | 0.5 | 0.3116 | nan |
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+ | 0.9539 | 4.0 | 60 | 1.1734 | 1.5561 | 1.5561 | 1.4238 | 1.4238 | 0.0580 | 0.0580 | 0.3913 | 0.0 | 0.5 | 0.3614 | nan |
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+ | 0.8665 | 5.0 | 75 | 0.8910 | 1.3560 | 1.3560 | 1.2350 | 1.2350 | 0.2847 | 0.2847 | 0.5652 | 0.0 | 0.5 | 0.4136 | nan |
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+ | 0.6564 | 6.0 | 90 | 0.8469 | 1.3220 | 1.3220 | 1.1570 | 1.1570 | 0.3201 | 0.3201 | 0.3913 | 0.0 | 0.5 | 0.3931 | nan |
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+ | 0.5241 | 7.0 | 105 | 0.6429 | 1.1519 | 1.1519 | 0.9757 | 0.9757 | 0.4838 | 0.4838 | 0.5652 | 0.0 | 0.5 | 0.4222 | nan |
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+ | 0.4589 | 8.0 | 120 | 0.5781 | 1.0923 | 1.0923 | 0.8714 | 0.8714 | 0.5359 | 0.5359 | 0.6522 | 0.0 | 0.5 | 0.4641 | nan |
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+ | 0.4043 | 9.0 | 135 | 0.4525 | 0.9664 | 0.9664 | 0.8257 | 0.8257 | 0.6367 | 0.6367 | 0.5652 | 0.0 | 0.5 | 0.4263 | nan |
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+ | 0.3498 | 10.0 | 150 | 0.4490 | 0.9627 | 0.9627 | 0.8272 | 0.8272 | 0.6395 | 0.6395 | 0.6522 | 0.0 | 0.5 | 0.5144 | nan |
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+ | 0.3505 | 11.0 | 165 | 0.3721 | 0.8763 | 0.8763 | 0.7471 | 0.7471 | 0.7013 | 0.7013 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
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+ | 0.3426 | 12.0 | 180 | 0.4117 | 0.9218 | 0.9218 | 0.7477 | 0.7477 | 0.6695 | 0.6695 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
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+ | 0.3074 | 13.0 | 195 | 0.3761 | 0.8810 | 0.8810 | 0.7109 | 0.7109 | 0.6981 | 0.6981 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
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+ | 0.2261 | 14.0 | 210 | 0.3818 | 0.8877 | 0.8877 | 0.7042 | 0.7042 | 0.6935 | 0.6935 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
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+ | 0.2399 | 15.0 | 225 | 0.3893 | 0.8964 | 0.8964 | 0.7108 | 0.7108 | 0.6874 | 0.6874 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
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+ | 0.2014 | 16.0 | 240 | 0.4606 | 0.9750 | 0.9750 | 0.8046 | 0.8046 | 0.6302 | 0.6302 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
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+ | 0.1937 | 17.0 | 255 | 0.4549 | 0.9689 | 0.9689 | 0.7679 | 0.7679 | 0.6348 | 0.6348 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
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+ | 0.1831 | 18.0 | 270 | 0.4113 | 0.9213 | 0.9213 | 0.6746 | 0.6746 | 0.6698 | 0.6698 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
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+ | 0.1758 | 19.0 | 285 | 0.4154 | 0.9259 | 0.9259 | 0.7053 | 0.7053 | 0.6665 | 0.6665 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
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+ | 0.1577 | 20.0 | 300 | 0.3970 | 0.9051 | 0.9051 | 0.7163 | 0.7163 | 0.6813 | 0.6813 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
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+ | 0.1597 | 21.0 | 315 | 0.4199 | 0.9309 | 0.9309 | 0.7270 | 0.7270 | 0.6629 | 0.6629 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
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+ | 0.1145 | 22.0 | 330 | 0.4250 | 0.9365 | 0.9365 | 0.6971 | 0.6971 | 0.6588 | 0.6588 | 0.8261 | 0.0 | 0.5 | 0.6594 | nan |
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+ | 0.1349 | 23.0 | 345 | 0.4168 | 0.9275 | 0.9275 | 0.7126 | 0.7126 | 0.6654 | 0.6654 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
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+ | 0.1481 | 24.0 | 360 | 0.4421 | 0.9552 | 0.9552 | 0.7441 | 0.7441 | 0.6451 | 0.6451 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
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+ | 0.1188 | 25.0 | 375 | 0.4356 | 0.9481 | 0.9481 | 0.7444 | 0.7444 | 0.6503 | 0.6503 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
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+ | 0.1119 | 26.0 | 390 | 0.4456 | 0.9590 | 0.9590 | 0.7139 | 0.7139 | 0.6422 | 0.6422 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
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+ | 0.1282 | 27.0 | 405 | 0.4456 | 0.9589 | 0.9589 | 0.7637 | 0.7637 | 0.6423 | 0.6423 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
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+ | 0.142 | 28.0 | 420 | 0.4501 | 0.9637 | 0.9637 | 0.7146 | 0.7146 | 0.6387 | 0.6387 | 0.8261 | 0.0 | 0.5 | 0.6594 | nan |
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+ | 0.126 | 29.0 | 435 | 0.4442 | 0.9575 | 0.9575 | 0.7189 | 0.7189 | 0.6433 | 0.6433 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
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+ | 0.1308 | 30.0 | 450 | 0.4439 | 0.9571 | 0.9571 | 0.7260 | 0.7260 | 0.6437 | 0.6437 | 0.7391 | 0.0 | 0.5 | 0.6287 | 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