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roberta-finetuned-token-reqadjinsiders

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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: roberta-finetuned-token-reqadjinsiders
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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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+ # roberta-finetuned-token-reqadjinsiders
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+
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+ This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0597
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+ - F1 B-cadj: 0
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+ - F1 I-cadj: 0.4734
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+ - F1 B-peso: 0
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+ - F1 I-peso: 0
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+ - Macro F1: 0.1183
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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: 0.0002
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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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: 200
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | F1 B-cadj | F1 I-cadj | F1 B-peso | F1 I-peso | Macro F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:---------:|:---------:|:---------:|:--------:|
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+ | 0.2188 | 1.0 | 10 | 0.0758 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0711 | 2.0 | 20 | 0.0678 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0656 | 3.0 | 30 | 0.0639 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0634 | 4.0 | 40 | 0.0629 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0624 | 5.0 | 50 | 0.0615 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0602 | 6.0 | 60 | 0.0598 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0573 | 7.0 | 70 | 0.0628 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0537 | 8.0 | 80 | 0.0531 | 0 | 0.1373 | 0 | 0 | 0.0343 |
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+ | 0.0595 | 9.0 | 90 | 0.0565 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0435 | 10.0 | 100 | 0.0659 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0507 | 11.0 | 110 | 0.0558 | 0 | 0.4549 | 0 | 0 | 0.1137 |
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+ | 0.0313 | 12.0 | 120 | 0.0561 | 0 | 0.3955 | 0 | 0 | 0.0989 |
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+ | 0.0278 | 13.0 | 130 | 0.0629 | 0 | 0.3756 | 0 | 0 | 0.0939 |
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+ | 0.0248 | 14.0 | 140 | 0.0634 | 0 | 0.3726 | 0 | 0 | 0.0932 |
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+ | 0.0282 | 15.0 | 150 | 0.0607 | 0 | 0.3303 | 0 | 0 | 0.0826 |
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+ | 0.0302 | 16.0 | 160 | 0.0628 | 0 | 0.4428 | 0 | 0 | 0.1107 |
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+ | 0.0205 | 17.0 | 170 | 0.0551 | 0 | 0.3855 | 0 | 0 | 0.0964 |
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+ | 0.0186 | 18.0 | 180 | 0.0627 | 0 | 0.4419 | 0 | 0 | 0.1105 |
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+ | 0.0171 | 19.0 | 190 | 0.0721 | 0 | 0.3524 | 0 | 0 | 0.0881 |
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+ | 0.0152 | 20.0 | 200 | 0.0574 | 0 | 0.3281 | 0 | 0 | 0.0820 |
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+ | 0.0152 | 21.0 | 210 | 0.0597 | 0 | 0.1515 | 0 | 0 | 0.0379 |
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+ | 0.0157 | 22.0 | 220 | 0.0675 | 0 | 0.3633 | 0 | 0 | 0.0908 |
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+ | 0.0135 | 23.0 | 230 | 0.0728 | 0 | 0.3135 | 0 | 0 | 0.0784 |
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+ | 0.0128 | 24.0 | 240 | 0.0703 | 0 | 0.4114 | 0 | 0 | 0.1028 |
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+ | 0.0126 | 25.0 | 250 | 0.0605 | 0 | 0.3695 | 0 | 0 | 0.0924 |
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+ | 0.0228 | 26.0 | 260 | 0.0490 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0266 | 27.0 | 270 | 0.0819 | 0 | 0.2214 | 0 | 0 | 0.0554 |
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+ | 0.0512 | 28.0 | 280 | 0.0598 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0625 | 29.0 | 290 | 0.0595 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0601 | 30.0 | 300 | 0.0593 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0595 | 31.0 | 310 | 0.0594 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0597 | 32.0 | 320 | 0.0593 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0594 | 33.0 | 330 | 0.0593 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0599 | 34.0 | 340 | 0.0593 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0601 | 35.0 | 350 | 0.0599 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0597 | 36.0 | 360 | 0.0593 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0595 | 37.0 | 370 | 0.0594 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0594 | 39.0 | 390 | 0.0593 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0581 | 66.0 | 660 | 0.0580 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0503 | 67.0 | 670 | 0.0608 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0338 | 68.0 | 680 | 0.0653 | 0 | 0.2763 | 0 | 0 | 0.0691 |
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+ | 0.0311 | 69.0 | 690 | 0.0727 | 0 | 0.3019 | 0 | 0 | 0.0755 |
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+ | 0.0305 | 70.0 | 700 | 0.0683 | 0 | 0.3515 | 0 | 0 | 0.0879 |
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+ | 0.0236 | 71.0 | 710 | 0.0757 | 0 | 0.2626 | 0 | 0 | 0.0656 |
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+ | 0.0261 | 72.0 | 720 | 0.0597 | 0 | 0.4734 | 0 | 0 | 0.1183 |
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+ | 0.0242 | 73.0 | 730 | 0.0621 | 0 | 0.4411 | 0 | 0 | 0.1103 |
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+ | 0.0293 | 74.0 | 740 | 0.0722 | 0 | 0.3305 | 0 | 0 | 0.0826 |
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200
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215
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216
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+ | 0.0591 | 167.0 | 1670 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
220
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221
+ | 0.0597 | 169.0 | 1690 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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223
+ | 0.059 | 171.0 | 1710 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.059 | 174.0 | 1740 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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228
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+ | 0.0591 | 177.0 | 1770 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0591 | 178.0 | 1780 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0592 | 179.0 | 1790 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.059 | 181.0 | 1810 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.059 | 183.0 | 1830 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0593 | 185.0 | 1850 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0591 | 186.0 | 1860 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0591 | 187.0 | 1870 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0598 | 188.0 | 1880 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0591 | 189.0 | 1890 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0591 | 190.0 | 1900 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0592 | 191.0 | 1910 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0599 | 192.0 | 1920 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0593 | 193.0 | 1930 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0595 | 194.0 | 1940 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0593 | 195.0 | 1950 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
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+ | 0.0592 | 196.0 | 1960 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
249
+ | 0.059 | 197.0 | 1970 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
250
+ | 0.0589 | 198.0 | 1980 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
251
+ | 0.0592 | 199.0 | 1990 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
252
+ | 0.0589 | 200.0 | 2000 | 0.0592 | 0 | 0 | 0 | 0 | 0.0 |
253
+
254
+
255
+ ### Framework versions
256
+
257
+ - Transformers 4.30.0.dev0
258
+ - Pytorch 2.0.0
259
+ - Datasets 2.1.0
260
+ - Tokenizers 0.13.3