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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-bert-focus-assassin |
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results: [] |
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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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# predict-perception-bert-focus-assassin |
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This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2964 |
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- Rmse: 0.8992 |
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- Rmse Focus::a Sull'assassino: 0.8992 |
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- Mae: 0.7331 |
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- Mae Focus::a Sull'assassino: 0.7331 |
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- R2: 0.6500 |
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- R2 Focus::a Sull'assassino: 0.6500 |
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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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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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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### Training results |
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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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| 1.0674 | 1.0 | 15 | 0.9851 | 1.6393 | 1.6393 | 1.5316 | 1.5316 | -0.1633 | -0.1633 | 0.1304 | 0.0 | 0.5 | 0.2457 | nan | |
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| 1.0099 | 2.0 | 30 | 0.8921 | 1.5601 | 1.5601 | 1.4317 | 1.4317 | -0.0535 | -0.0535 | 0.5652 | 0.0 | 0.5 | 0.4734 | nan | |
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| 0.9295 | 3.0 | 45 | 0.7345 | 1.4155 | 1.4155 | 1.3113 | 1.3113 | 0.1327 | 0.1327 | 0.5652 | 0.0 | 0.5 | 0.3596 | nan | |
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| 0.8485 | 4.0 | 60 | 0.7282 | 1.4094 | 1.4094 | 1.2678 | 1.2678 | 0.1401 | 0.1401 | 0.7391 | 0.0 | 0.5 | 0.5367 | nan | |
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| 0.7551 | 5.0 | 75 | 0.5966 | 1.2758 | 1.2758 | 1.1144 | 1.1144 | 0.2955 | 0.2955 | 0.6522 | 0.0 | 0.5 | 0.3911 | nan | |
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| 0.5563 | 6.0 | 90 | 0.4578 | 1.1175 | 1.1175 | 0.9105 | 0.9105 | 0.4594 | 0.4594 | 0.6522 | 0.0 | 0.5 | 0.3911 | nan | |
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| 0.4048 | 7.0 | 105 | 0.3539 | 0.9826 | 0.9826 | 0.7770 | 0.7770 | 0.5821 | 0.5821 | 0.6522 | 0.0 | 0.5 | 0.5522 | nan | |
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| 0.3319 | 8.0 | 120 | 0.2938 | 0.8953 | 0.8953 | 0.7110 | 0.7110 | 0.6530 | 0.6530 | 0.6522 | 0.0 | 0.5 | 0.6021 | nan | |
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| 0.2224 | 9.0 | 135 | 0.3455 | 0.9708 | 0.9708 | 0.7607 | 0.7607 | 0.5921 | 0.5921 | 0.6522 | 0.0 | 0.5 | 0.3911 | nan | |
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| 0.1794 | 10.0 | 150 | 0.2719 | 0.8612 | 0.8612 | 0.6768 | 0.6768 | 0.6790 | 0.6790 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.1553 | 11.0 | 165 | 0.2855 | 0.8826 | 0.8826 | 0.7053 | 0.7053 | 0.6628 | 0.6628 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.1008 | 12.0 | 180 | 0.3000 | 0.9046 | 0.9046 | 0.7255 | 0.7255 | 0.6458 | 0.6458 | 0.6522 | 0.0 | 0.5 | 0.5261 | nan | |
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| 0.1121 | 13.0 | 195 | 0.2817 | 0.8766 | 0.8766 | 0.7236 | 0.7236 | 0.6674 | 0.6674 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.08 | 14.0 | 210 | 0.3504 | 0.9777 | 0.9777 | 0.7631 | 0.7631 | 0.5863 | 0.5863 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.0802 | 15.0 | 225 | 0.3031 | 0.9094 | 0.9094 | 0.7565 | 0.7565 | 0.6420 | 0.6420 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.0685 | 16.0 | 240 | 0.3041 | 0.9109 | 0.9109 | 0.7409 | 0.7409 | 0.6408 | 0.6408 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.0592 | 17.0 | 255 | 0.3496 | 0.9767 | 0.9767 | 0.7812 | 0.7812 | 0.5871 | 0.5871 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.0625 | 18.0 | 270 | 0.3260 | 0.9430 | 0.9430 | 0.7757 | 0.7757 | 0.6151 | 0.6151 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.0589 | 19.0 | 285 | 0.3118 | 0.9222 | 0.9222 | 0.7442 | 0.7442 | 0.6318 | 0.6318 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.0518 | 20.0 | 300 | 0.3062 | 0.9140 | 0.9140 | 0.7459 | 0.7459 | 0.6384 | 0.6384 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.0456 | 21.0 | 315 | 0.3200 | 0.9344 | 0.9344 | 0.7592 | 0.7592 | 0.6221 | 0.6221 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.0477 | 22.0 | 330 | 0.3132 | 0.9244 | 0.9244 | 0.7532 | 0.7532 | 0.6301 | 0.6301 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.0448 | 23.0 | 345 | 0.3006 | 0.9056 | 0.9056 | 0.7321 | 0.7321 | 0.6450 | 0.6450 | 0.6522 | 0.0 | 0.5 | 0.5261 | nan | |
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| 0.0494 | 24.0 | 360 | 0.2985 | 0.9024 | 0.9024 | 0.7463 | 0.7463 | 0.6475 | 0.6475 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.0369 | 25.0 | 375 | 0.3039 | 0.9105 | 0.9105 | 0.7359 | 0.7359 | 0.6412 | 0.6412 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.0456 | 26.0 | 390 | 0.2989 | 0.9030 | 0.9030 | 0.7210 | 0.7210 | 0.6471 | 0.6471 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.044 | 27.0 | 405 | 0.2997 | 0.9042 | 0.9042 | 0.7418 | 0.7418 | 0.6461 | 0.6461 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.0352 | 28.0 | 420 | 0.2970 | 0.9001 | 0.9001 | 0.7346 | 0.7346 | 0.6493 | 0.6493 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.0429 | 29.0 | 435 | 0.2970 | 0.9001 | 0.9001 | 0.7281 | 0.7281 | 0.6493 | 0.6493 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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| 0.0378 | 30.0 | 450 | 0.2964 | 0.8992 | 0.8992 | 0.7331 | 0.7331 | 0.6500 | 0.6500 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | |
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### Framework versions |
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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 |
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