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predict-perception-xlmr-focus-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-focus-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-focus-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: 0.3264
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+ - Rmse: 0.9437
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+ - Rmse Focus::a Sull'assassino: 0.9437
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+ - Mae: 0.7093
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+ - Mae Focus::a Sull'assassino: 0.7093
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+ - R2: 0.6145
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+ - R2 Focus::a Sull'assassino: 0.6145
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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: 0.6131
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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 Sull'assassino | Mae | Mae Focus::a Sull'assassino | R2 | R2 Focus::a Sull'assassino | Cos | Pair | Rank | Neighbors | Rsa |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:----------------------------:|:------:|:---------------------------:|:-------:|:--------------------------:|:-------:|:----:|:----:|:---------:|:---:|
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+ | 1.0403 | 1.0 | 15 | 1.1576 | 1.7771 | 1.7771 | 1.6028 | 1.6028 | -0.3670 | -0.3670 | -0.2174 | 0.0 | 0.5 | 0.2379 | nan |
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+ | 0.9818 | 2.0 | 30 | 0.8916 | 1.5596 | 1.5596 | 1.4136 | 1.4136 | -0.0529 | -0.0529 | 0.3913 | 0.0 | 0.5 | 0.3793 | nan |
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+ | 0.9276 | 3.0 | 45 | 0.9277 | 1.5909 | 1.5909 | 1.4560 | 1.4560 | -0.0955 | -0.0955 | 0.3913 | 0.0 | 0.5 | 0.3742 | nan |
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+ | 0.8395 | 4.0 | 60 | 0.7958 | 1.4734 | 1.4734 | 1.3032 | 1.3032 | 0.0603 | 0.0603 | 0.5652 | 0.0 | 0.5 | 0.4598 | nan |
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+ | 0.7587 | 5.0 | 75 | 0.4647 | 1.1259 | 1.1259 | 0.9316 | 0.9316 | 0.4513 | 0.4513 | 0.6522 | 0.0 | 0.5 | 0.5087 | nan |
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+ | 0.696 | 6.0 | 90 | 0.5368 | 1.2101 | 1.2101 | 1.0847 | 1.0847 | 0.3661 | 0.3661 | 0.7391 | 0.0 | 0.5 | 0.5302 | nan |
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+ | 0.548 | 7.0 | 105 | 0.3110 | 0.9211 | 0.9211 | 0.7896 | 0.7896 | 0.6328 | 0.6328 | 0.6522 | 0.0 | 0.5 | 0.5261 | nan |
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+ | 0.4371 | 8.0 | 120 | 0.3392 | 0.9619 | 0.9619 | 0.8132 | 0.8132 | 0.5995 | 0.5995 | 0.6522 | 0.0 | 0.5 | 0.5261 | nan |
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+ | 0.355 | 9.0 | 135 | 0.3938 | 1.0366 | 1.0366 | 0.8153 | 0.8153 | 0.5349 | 0.5349 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
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+ | 0.2919 | 10.0 | 150 | 0.3484 | 0.9749 | 0.9749 | 0.7487 | 0.7487 | 0.5886 | 0.5886 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
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+ | 0.2595 | 11.0 | 165 | 0.2812 | 0.8759 | 0.8759 | 0.6265 | 0.6265 | 0.6679 | 0.6679 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
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+ | 0.2368 | 12.0 | 180 | 0.2534 | 0.8314 | 0.8314 | 0.6402 | 0.6402 | 0.7008 | 0.7008 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
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+ | 0.227 | 13.0 | 195 | 0.2878 | 0.8861 | 0.8861 | 0.6769 | 0.6769 | 0.6601 | 0.6601 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
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+ | 0.1979 | 14.0 | 210 | 0.2405 | 0.8100 | 0.8100 | 0.6113 | 0.6113 | 0.7160 | 0.7160 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
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+ | 0.1622 | 15.0 | 225 | 0.2575 | 0.8382 | 0.8382 | 0.6017 | 0.6017 | 0.6959 | 0.6959 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.1575 | 16.0 | 240 | 0.2945 | 0.8963 | 0.8963 | 0.6741 | 0.6741 | 0.6523 | 0.6523 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.1479 | 17.0 | 255 | 0.3563 | 0.9859 | 0.9859 | 0.7367 | 0.7367 | 0.5792 | 0.5792 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.1269 | 18.0 | 270 | 0.2806 | 0.8750 | 0.8750 | 0.6665 | 0.6665 | 0.6686 | 0.6686 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.1257 | 19.0 | 285 | 0.3267 | 0.9441 | 0.9441 | 0.6739 | 0.6739 | 0.6142 | 0.6142 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.134 | 20.0 | 300 | 0.3780 | 1.0155 | 1.0155 | 0.7331 | 0.7331 | 0.5536 | 0.5536 | 0.7391 | 0.0 | 0.5 | 0.5302 | nan |
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+ | 0.1171 | 21.0 | 315 | 0.3890 | 1.0301 | 1.0301 | 0.7444 | 0.7444 | 0.5406 | 0.5406 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.0934 | 22.0 | 330 | 0.3131 | 0.9242 | 0.9242 | 0.6923 | 0.6923 | 0.6303 | 0.6303 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.1112 | 23.0 | 345 | 0.2912 | 0.8913 | 0.8913 | 0.6610 | 0.6610 | 0.6561 | 0.6561 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.1038 | 24.0 | 360 | 0.3109 | 0.9209 | 0.9209 | 0.7019 | 0.7019 | 0.6329 | 0.6329 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.085 | 25.0 | 375 | 0.3469 | 0.9728 | 0.9728 | 0.7383 | 0.7383 | 0.5904 | 0.5904 | 0.8261 | 0.0 | 0.5 | 0.6622 | nan |
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+ | 0.0843 | 26.0 | 390 | 0.3017 | 0.9073 | 0.9073 | 0.6848 | 0.6848 | 0.6437 | 0.6437 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
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+ | 0.093 | 27.0 | 405 | 0.3269 | 0.9443 | 0.9443 | 0.7042 | 0.7042 | 0.6140 | 0.6140 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
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+ | 0.0846 | 28.0 | 420 | 0.3161 | 0.9286 | 0.9286 | 0.6937 | 0.6937 | 0.6267 | 0.6267 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
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+ | 0.0764 | 29.0 | 435 | 0.3244 | 0.9408 | 0.9408 | 0.7079 | 0.7079 | 0.6169 | 0.6169 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
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+ | 0.0697 | 30.0 | 450 | 0.3264 | 0.9437 | 0.9437 | 0.7093 | 0.7093 | 0.6145 | 0.6145 | 0.7391 | 0.0 | 0.5 | 0.6131 | 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