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predict-perception-xlmr-blame-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-blame-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-blame-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: 1.1098
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+ - Rmse: 0.6801
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+ - Rmse Blame::a La vittima: 0.6801
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+ - Mae: 0.5617
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+ - Mae Blame::a La vittima: 0.5617
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+ - R2: -1.5910
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+ - R2 Blame::a La vittima: -1.5910
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+ - Cos: -0.1304
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+ - Pair: 0.0
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+ - Rank: 0.5
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+ - Neighbors: 0.3333
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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 La vittima | Mae | Mae Blame::a La vittima | R2 | R2 Blame::a La vittima | Cos | Pair | Rank | Neighbors | Rsa |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------:|:------:|:-----------------------:|:-------:|:----------------------:|:-------:|:----:|:----:|:---------:|:---:|
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+ | 1.0422 | 1.0 | 15 | 0.4952 | 0.4542 | 0.4542 | 0.4095 | 0.4095 | -0.1560 | -0.1560 | -0.1304 | 0.0 | 0.5 | 0.2971 | nan |
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+ | 1.0434 | 2.0 | 30 | 0.4851 | 0.4496 | 0.4496 | 0.4054 | 0.4054 | -0.1324 | -0.1324 | -0.1304 | 0.0 | 0.5 | 0.2971 | nan |
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+ | 1.038 | 3.0 | 45 | 0.4513 | 0.4337 | 0.4337 | 0.3885 | 0.3885 | -0.0536 | -0.0536 | -0.1304 | 0.0 | 0.5 | 0.2971 | nan |
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+ | 1.0151 | 4.0 | 60 | 0.4395 | 0.4280 | 0.4280 | 0.3840 | 0.3840 | -0.0262 | -0.0262 | -0.1304 | 0.0 | 0.5 | 0.2715 | nan |
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+ | 0.9727 | 5.0 | 75 | 0.4490 | 0.4325 | 0.4325 | 0.3811 | 0.3811 | -0.0482 | -0.0482 | 0.2174 | 0.0 | 0.5 | 0.3338 | nan |
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+ | 0.9733 | 6.0 | 90 | 0.4540 | 0.4349 | 0.4349 | 0.3860 | 0.3860 | -0.0598 | -0.0598 | -0.2174 | 0.0 | 0.5 | 0.3248 | nan |
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+ | 0.9396 | 7.0 | 105 | 0.4501 | 0.4331 | 0.4331 | 0.3849 | 0.3849 | -0.0508 | -0.0508 | 0.0435 | 0.0 | 0.5 | 0.2609 | nan |
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+ | 0.8759 | 8.0 | 120 | 0.4597 | 0.4377 | 0.4377 | 0.3849 | 0.3849 | -0.0731 | -0.0731 | 0.3043 | 0.0 | 0.5 | 0.3898 | nan |
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+ | 0.8768 | 9.0 | 135 | 0.4575 | 0.4366 | 0.4366 | 0.3784 | 0.3784 | -0.0680 | -0.0680 | 0.4783 | 0.0 | 0.5 | 0.4615 | nan |
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+ | 0.8312 | 10.0 | 150 | 0.5363 | 0.4727 | 0.4727 | 0.4071 | 0.4071 | -0.2520 | -0.2520 | -0.0435 | 0.0 | 0.5 | 0.2733 | nan |
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+ | 0.7296 | 11.0 | 165 | 0.5291 | 0.4696 | 0.4696 | 0.4057 | 0.4057 | -0.2353 | -0.2353 | 0.3043 | 0.0 | 0.5 | 0.3898 | nan |
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+ | 0.7941 | 12.0 | 180 | 0.5319 | 0.4708 | 0.4708 | 0.4047 | 0.4047 | -0.2417 | -0.2417 | 0.1304 | 0.0 | 0.5 | 0.3381 | nan |
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+ | 0.6486 | 13.0 | 195 | 0.6787 | 0.5318 | 0.5318 | 0.4516 | 0.4516 | -0.5846 | -0.5846 | 0.1304 | 0.0 | 0.5 | 0.3381 | nan |
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+ | 0.6241 | 14.0 | 210 | 1.0146 | 0.6502 | 0.6502 | 0.5580 | 0.5580 | -1.3687 | -1.3687 | -0.1304 | 0.0 | 0.5 | 0.3509 | nan |
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+ | 0.5868 | 15.0 | 225 | 0.7164 | 0.5464 | 0.5464 | 0.4682 | 0.4682 | -0.6725 | -0.6725 | -0.0435 | 0.0 | 0.5 | 0.3333 | nan |
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+ | 0.5305 | 16.0 | 240 | 0.9064 | 0.6146 | 0.6146 | 0.5173 | 0.5173 | -1.1161 | -1.1161 | -0.0435 | 0.0 | 0.5 | 0.3333 | nan |
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+ | 0.495 | 17.0 | 255 | 1.3860 | 0.7600 | 0.7600 | 0.6433 | 0.6433 | -2.2358 | -2.2358 | -0.0435 | 0.0 | 0.5 | 0.2935 | nan |
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+ | 0.566 | 18.0 | 270 | 0.7618 | 0.5634 | 0.5634 | 0.4730 | 0.4730 | -0.7785 | -0.7785 | 0.0435 | 0.0 | 0.5 | 0.3225 | nan |
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+ | 0.4305 | 19.0 | 285 | 0.8849 | 0.6072 | 0.6072 | 0.5048 | 0.5048 | -1.0659 | -1.0659 | -0.0435 | 0.0 | 0.5 | 0.3333 | nan |
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+ | 0.5108 | 20.0 | 300 | 0.7376 | 0.5544 | 0.5544 | 0.4716 | 0.4716 | -0.7220 | -0.7220 | 0.0435 | 0.0 | 0.5 | 0.3225 | nan |
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+ | 0.44 | 21.0 | 315 | 1.1611 | 0.6956 | 0.6956 | 0.5921 | 0.5921 | -1.7108 | -1.7108 | -0.1304 | 0.0 | 0.5 | 0.3333 | nan |
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+ | 0.395 | 22.0 | 330 | 1.3004 | 0.7361 | 0.7361 | 0.6078 | 0.6078 | -2.0360 | -2.0360 | -0.2174 | 0.0 | 0.5 | 0.3587 | nan |
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+ | 0.3945 | 23.0 | 345 | 0.9376 | 0.6251 | 0.6251 | 0.5272 | 0.5272 | -1.1890 | -1.1890 | -0.2174 | 0.0 | 0.5 | 0.3188 | nan |
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+ | 0.3093 | 24.0 | 360 | 1.3586 | 0.7524 | 0.7524 | 0.6219 | 0.6219 | -2.1719 | -2.1719 | -0.2174 | 0.0 | 0.5 | 0.3587 | nan |
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+ | 0.2676 | 25.0 | 375 | 1.2200 | 0.7130 | 0.7130 | 0.5994 | 0.5994 | -1.8484 | -1.8484 | -0.2174 | 0.0 | 0.5 | 0.3587 | nan |
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+ | 0.3257 | 26.0 | 390 | 1.2235 | 0.7140 | 0.7140 | 0.5900 | 0.5900 | -1.8564 | -1.8564 | -0.2174 | 0.0 | 0.5 | 0.3587 | nan |
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+ | 0.4004 | 27.0 | 405 | 1.0978 | 0.6763 | 0.6763 | 0.5624 | 0.5624 | -1.5629 | -1.5629 | -0.2174 | 0.0 | 0.5 | 0.3587 | nan |
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+ | 0.283 | 28.0 | 420 | 1.1454 | 0.6909 | 0.6909 | 0.5697 | 0.5697 | -1.6742 | -1.6742 | -0.2174 | 0.0 | 0.5 | 0.3587 | nan |
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+ | 0.3326 | 29.0 | 435 | 1.1214 | 0.6836 | 0.6836 | 0.5646 | 0.5646 | -1.6181 | -1.6181 | -0.1304 | 0.0 | 0.5 | 0.3333 | nan |
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+ | 0.2632 | 30.0 | 450 | 1.1098 | 0.6801 | 0.6801 | 0.5617 | 0.5617 | -1.5910 | -1.5910 | -0.1304 | 0.0 | 0.5 | 0.3333 | 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