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predict-perception-xlmr-blame-object

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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-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-xlmr-blame-object
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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: 0.7219
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+ - Rmse: 0.6215
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+ - Rmse Blame::a Un oggetto: 0.6215
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+ - Mae: 0.4130
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+ - Mae Blame::a Un oggetto: 0.4130
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+ - R2: 0.1200
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+ - R2 Blame::a Un oggetto: 0.1200
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+ - Cos: 0.3043
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+ - Pair: 0.0
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+ - Rank: 0.5
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+ - Neighbors: 0.4335
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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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+ | 1.0279 | 1.0 | 15 | 0.8483 | 0.6737 | 0.6737 | 0.4761 | 0.4761 | -0.0341 | -0.0341 | -0.3043 | 0.0 | 0.5 | 0.5507 | nan |
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+ | 1.0676 | 2.0 | 30 | 0.7749 | 0.6439 | 0.6439 | 0.4291 | 0.4291 | 0.0554 | 0.0554 | 0.0435 | 0.0 | 0.5 | 0.2614 | nan |
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+ | 0.9563 | 3.0 | 45 | 0.7765 | 0.6446 | 0.6446 | 0.4349 | 0.4349 | 0.0535 | 0.0535 | -0.0435 | 0.0 | 0.5 | 0.4515 | nan |
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+ | 0.9622 | 4.0 | 60 | 0.7443 | 0.6311 | 0.6311 | 0.4061 | 0.4061 | 0.0927 | 0.0927 | 0.1304 | 0.0 | 0.5 | 0.2933 | nan |
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+ | 0.948 | 5.0 | 75 | 0.8071 | 0.6571 | 0.6571 | 0.3817 | 0.3817 | 0.0162 | 0.0162 | 0.3043 | 0.0 | 0.5 | 0.4207 | nan |
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+ | 0.9532 | 6.0 | 90 | 0.8007 | 0.6546 | 0.6546 | 0.4585 | 0.4585 | 0.0239 | 0.0239 | -0.0435 | 0.0 | 0.5 | 0.5507 | nan |
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+ | 0.9101 | 7.0 | 105 | 0.7126 | 0.6175 | 0.6175 | 0.3649 | 0.3649 | 0.1313 | 0.1313 | 0.4783 | 0.0 | 0.5 | 0.6012 | nan |
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+ | 0.8369 | 8.0 | 120 | 0.7194 | 0.6204 | 0.6204 | 0.3896 | 0.3896 | 0.1231 | 0.1231 | 0.3913 | 0.0 | 0.5 | 0.3494 | nan |
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+ | 0.8062 | 9.0 | 135 | 0.7157 | 0.6188 | 0.6188 | 0.4192 | 0.4192 | 0.1275 | 0.1275 | 0.0435 | 0.0 | 0.5 | 0.3182 | nan |
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+ | 0.7344 | 10.0 | 150 | 0.7161 | 0.6190 | 0.6190 | 0.3612 | 0.3612 | 0.1270 | 0.1270 | 0.3043 | 0.0 | 0.5 | 0.6035 | nan |
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+ | 0.7439 | 11.0 | 165 | 0.5894 | 0.5616 | 0.5616 | 0.3723 | 0.3723 | 0.2816 | 0.2816 | 0.3043 | 0.0 | 0.5 | 0.3846 | nan |
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+ | 0.6241 | 12.0 | 180 | 0.7087 | 0.6158 | 0.6158 | 0.3972 | 0.3972 | 0.1361 | 0.1361 | 0.3043 | 0.0 | 0.5 | 0.3846 | nan |
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+ | 0.6123 | 13.0 | 195 | 0.6318 | 0.5814 | 0.5814 | 0.3673 | 0.3673 | 0.2298 | 0.2298 | 0.3913 | 0.0 | 0.5 | 0.4413 | nan |
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+ | 0.5364 | 14.0 | 210 | 0.6504 | 0.5899 | 0.5899 | 0.3674 | 0.3674 | 0.2072 | 0.2072 | 0.3043 | 0.0 | 0.5 | 0.3846 | nan |
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+ | 0.5586 | 15.0 | 225 | 0.7151 | 0.6186 | 0.6186 | 0.3850 | 0.3850 | 0.1283 | 0.1283 | 0.3043 | 0.0 | 0.5 | 0.4335 | nan |
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+ | 0.5133 | 16.0 | 240 | 0.5572 | 0.5460 | 0.5460 | 0.3540 | 0.3540 | 0.3208 | 0.3208 | 0.4783 | 0.0 | 0.5 | 0.5314 | nan |
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+ | 0.4193 | 17.0 | 255 | 0.6047 | 0.5688 | 0.5688 | 0.3710 | 0.3710 | 0.2629 | 0.2629 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
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+ | 0.3504 | 18.0 | 270 | 0.6103 | 0.5714 | 0.5714 | 0.3687 | 0.3687 | 0.2561 | 0.2561 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
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+ | 0.3328 | 19.0 | 285 | 0.6181 | 0.5751 | 0.5751 | 0.3915 | 0.3915 | 0.2466 | 0.2466 | 0.4783 | 0.0 | 0.5 | 0.5314 | nan |
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+ | 0.3276 | 20.0 | 300 | 0.6334 | 0.5822 | 0.5822 | 0.3612 | 0.3612 | 0.2279 | 0.2279 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
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+ | 0.3271 | 21.0 | 315 | 0.6200 | 0.5760 | 0.5760 | 0.3827 | 0.3827 | 0.2442 | 0.2442 | 0.3043 | 0.0 | 0.5 | 0.4335 | nan |
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+ | 0.3139 | 22.0 | 330 | 0.6332 | 0.5821 | 0.5821 | 0.3723 | 0.3723 | 0.2281 | 0.2281 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
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+ | 0.2872 | 23.0 | 345 | 0.6694 | 0.5985 | 0.5985 | 0.3966 | 0.3966 | 0.1840 | 0.1840 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
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+ | 0.3617 | 24.0 | 360 | 0.7022 | 0.6130 | 0.6130 | 0.4061 | 0.4061 | 0.1440 | 0.1440 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
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+ | 0.3227 | 25.0 | 375 | 0.7364 | 0.6277 | 0.6277 | 0.4205 | 0.4205 | 0.1024 | 0.1024 | 0.3043 | 0.0 | 0.5 | 0.4335 | nan |
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+ | 0.256 | 26.0 | 390 | 0.6938 | 0.6093 | 0.6093 | 0.3833 | 0.3833 | 0.1543 | 0.1543 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
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+ | 0.2605 | 27.0 | 405 | 0.7221 | 0.6216 | 0.6216 | 0.4036 | 0.4036 | 0.1198 | 0.1198 | 0.3043 | 0.0 | 0.5 | 0.4335 | nan |
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+ | 0.2558 | 28.0 | 420 | 0.6959 | 0.6102 | 0.6102 | 0.3859 | 0.3859 | 0.1518 | 0.1518 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
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+ | 0.2403 | 29.0 | 435 | 0.7152 | 0.6186 | 0.6186 | 0.4088 | 0.4088 | 0.1281 | 0.1281 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
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+ | 0.3263 | 30.0 | 450 | 0.7219 | 0.6215 | 0.6215 | 0.4130 | 0.4130 | 0.1200 | 0.1200 | 0.3043 | 0.0 | 0.5 | 0.4335 | 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