--- license: mit tags: - generated_from_trainer model-index: - name: predict-perception-xlmr-focus-object results: [] --- # predict-perception-xlmr-focus-object This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1927 - Rmse: 0.5495 - Rmse Focus::a Su un oggetto: 0.5495 - Mae: 0.4174 - Mae Focus::a Su un oggetto: 0.4174 - R2: 0.5721 - R2 Focus::a Su un oggetto: 0.5721 - Cos: 0.5652 - Pair: 0.0 - Rank: 0.5 - Neighbors: 0.5518 - Rsa: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 20 - eval_batch_size: 8 - seed: 1996 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Focus::a Su un oggetto | Mae | Mae Focus::a Su un oggetto | R2 | R2 Focus::a Su un oggetto | Cos | Pair | Rank | Neighbors | Rsa | |:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:-------:|:-------------------------:|:-------:|:----:|:----:|:---------:|:---:| | 1.0316 | 1.0 | 15 | 0.6428 | 1.0035 | 1.0035 | 0.8806 | 0.8806 | -0.4272 | -0.4272 | -0.4783 | 0.0 | 0.5 | 0.5302 | nan | | 1.0005 | 2.0 | 30 | 0.4564 | 0.8456 | 0.8456 | 0.7078 | 0.7078 | -0.0134 | -0.0134 | 0.4783 | 0.0 | 0.5 | 0.4440 | nan | | 0.9519 | 3.0 | 45 | 0.4151 | 0.8063 | 0.8063 | 0.6797 | 0.6797 | 0.0784 | 0.0784 | 0.1304 | 0.0 | 0.5 | 0.4888 | nan | | 0.92 | 4.0 | 60 | 0.3982 | 0.7898 | 0.7898 | 0.6516 | 0.6516 | 0.1159 | 0.1159 | 0.2174 | 0.0 | 0.5 | 0.5036 | nan | | 0.8454 | 5.0 | 75 | 0.2739 | 0.6550 | 0.6550 | 0.5292 | 0.5292 | 0.3919 | 0.3919 | 0.6522 | 0.0 | 0.5 | 0.4160 | nan | | 0.7247 | 6.0 | 90 | 0.2413 | 0.6148 | 0.6148 | 0.5347 | 0.5347 | 0.4642 | 0.4642 | 0.4783 | 0.0 | 0.5 | 0.3453 | nan | | 0.6055 | 7.0 | 105 | 0.3109 | 0.6978 | 0.6978 | 0.6115 | 0.6115 | 0.3098 | 0.3098 | 0.4783 | 0.0 | 0.5 | 0.4154 | nan | | 0.5411 | 8.0 | 120 | 0.3932 | 0.7848 | 0.7848 | 0.6712 | 0.6712 | 0.1271 | 0.1271 | 0.4783 | 0.0 | 0.5 | 0.4154 | nan | | 0.4784 | 9.0 | 135 | 0.1316 | 0.4540 | 0.4540 | 0.3750 | 0.3750 | 0.7079 | 0.7079 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.4039 | 10.0 | 150 | 0.2219 | 0.5896 | 0.5896 | 0.4954 | 0.4954 | 0.5074 | 0.5074 | 0.5652 | 0.0 | 0.5 | 0.4838 | nan | | 0.3415 | 11.0 | 165 | 0.1935 | 0.5505 | 0.5505 | 0.4443 | 0.4443 | 0.5704 | 0.5704 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.3369 | 12.0 | 180 | 0.2118 | 0.5761 | 0.5761 | 0.4554 | 0.4554 | 0.5296 | 0.5296 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.3083 | 13.0 | 195 | 0.1928 | 0.5496 | 0.5496 | 0.4368 | 0.4368 | 0.5718 | 0.5718 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.2678 | 14.0 | 210 | 0.2205 | 0.5877 | 0.5877 | 0.4472 | 0.4472 | 0.5105 | 0.5105 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.2199 | 15.0 | 225 | 0.2118 | 0.5760 | 0.5760 | 0.4689 | 0.4689 | 0.5297 | 0.5297 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.2238 | 16.0 | 240 | 0.2461 | 0.6209 | 0.6209 | 0.5047 | 0.5047 | 0.4537 | 0.4537 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.2233 | 17.0 | 255 | 0.2307 | 0.6011 | 0.6011 | 0.4618 | 0.4618 | 0.4879 | 0.4879 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.1903 | 18.0 | 270 | 0.2207 | 0.5880 | 0.5880 | 0.4432 | 0.4432 | 0.5100 | 0.5100 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan | | 0.1714 | 19.0 | 285 | 0.2146 | 0.5798 | 0.5798 | 0.4368 | 0.4368 | 0.5236 | 0.5236 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.1759 | 20.0 | 300 | 0.1745 | 0.5228 | 0.5228 | 0.4152 | 0.4152 | 0.6126 | 0.6126 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.1505 | 21.0 | 315 | 0.1944 | 0.5519 | 0.5519 | 0.4170 | 0.4170 | 0.5684 | 0.5684 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.1467 | 22.0 | 330 | 0.1802 | 0.5313 | 0.5313 | 0.3910 | 0.3910 | 0.5999 | 0.5999 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan | | 0.1441 | 23.0 | 345 | 0.2360 | 0.6081 | 0.6081 | 0.4755 | 0.4755 | 0.4760 | 0.4760 | 0.4783 | 0.0 | 0.5 | 0.4938 | nan | | 0.1553 | 24.0 | 360 | 0.2129 | 0.5774 | 0.5774 | 0.4539 | 0.4539 | 0.5274 | 0.5274 | 0.5652 | 0.0 | 0.5 | 0.5518 | nan | | 0.1163 | 25.0 | 375 | 0.1780 | 0.5281 | 0.5281 | 0.3952 | 0.3952 | 0.6048 | 0.6048 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan | | 0.1266 | 26.0 | 390 | 0.2163 | 0.5821 | 0.5821 | 0.4569 | 0.4569 | 0.5198 | 0.5198 | 0.5652 | 0.0 | 0.5 | 0.5518 | nan | | 0.1416 | 27.0 | 405 | 0.1829 | 0.5352 | 0.5352 | 0.4082 | 0.4082 | 0.5939 | 0.5939 | 0.5652 | 0.0 | 0.5 | 0.5518 | nan | | 0.1576 | 28.0 | 420 | 0.1930 | 0.5498 | 0.5498 | 0.4126 | 0.4126 | 0.5716 | 0.5716 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan | | 0.118 | 29.0 | 435 | 0.2070 | 0.5694 | 0.5694 | 0.4378 | 0.4378 | 0.5405 | 0.5405 | 0.5652 | 0.0 | 0.5 | 0.5518 | nan | | 0.1179 | 30.0 | 450 | 0.1927 | 0.5495 | 0.5495 | 0.4174 | 0.4174 | 0.5721 | 0.5721 | 0.5652 | 0.0 | 0.5 | 0.5518 | nan | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0