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# beto-finetuned-token-reqadjinsiders

This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7385
- Precision: 0.0833
- Recall: 0.1
- F1: 0.0909
- Accuracy: 0.9092

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5869        | 1.0   | 10   | 0.4001          | 0.0       | 0.0    | 0.0    | 0.8196   |
| 0.2986        | 2.0   | 20   | 0.4095          | 0.0       | 0.0    | 0.0    | 0.8876   |
| 0.2215        | 3.0   | 30   | 0.3336          | 0.0       | 0.0    | 0.0    | 0.8643   |
| 0.1356        | 4.0   | 40   | 0.3362          | 0.0       | 0.0    | 0.0    | 0.8954   |
| 0.0717        | 5.0   | 50   | 0.3489          | 0.0       | 0.0    | 0.0    | 0.8987   |
| 0.0424        | 6.0   | 60   | 0.4066          | 0.0       | 0.0    | 0.0    | 0.9044   |
| 0.0301        | 7.0   | 70   | 0.3172          | 0.0741    | 0.1    | 0.0851 | 0.9227   |
| 0.0191        | 8.0   | 80   | 0.5007          | 0.0435    | 0.05   | 0.0465 | 0.9050   |
| 0.0155        | 9.0   | 90   | 0.5146          | 0.1       | 0.05   | 0.0667 | 0.9133   |
| 0.0174        | 10.0  | 100  | 0.3293          | 0.0465    | 0.1    | 0.0635 | 0.9122   |
| 0.0113        | 11.0  | 110  | 0.4793          | 0.0714    | 0.1    | 0.0833 | 0.9179   |
| 0.0136        | 12.0  | 120  | 0.4758          | 0.1905    | 0.2    | 0.1951 | 0.9259   |
| 0.0095        | 13.0  | 130  | 0.3407          | 0.0571    | 0.1    | 0.0727 | 0.9231   |
| 0.0113        | 14.0  | 140  | 0.3864          | 0.0833    | 0.1    | 0.0909 | 0.9076   |
| 0.0036        | 15.0  | 150  | 0.4718          | 0.0741    | 0.1    | 0.0851 | 0.9096   |
| 0.0036        | 16.0  | 160  | 0.5261          | 0.0882    | 0.15   | 0.1111 | 0.8965   |
| 0.0021        | 17.0  | 170  | 0.6655          | 0.0417    | 0.05   | 0.0455 | 0.8902   |
| 0.0033        | 18.0  | 180  | 0.5417          | 0.1212    | 0.2    | 0.1509 | 0.9054   |
| 0.0023        | 19.0  | 190  | 0.6521          | 0.1111    | 0.1    | 0.1053 | 0.9083   |
| 0.0021        | 20.0  | 200  | 0.4450          | 0.0909    | 0.15   | 0.1132 | 0.9214   |
| 0.0038        | 21.0  | 210  | 0.5652          | 0.1429    | 0.1    | 0.1176 | 0.9194   |
| 0.0088        | 22.0  | 220  | 0.5791          | 0.0833    | 0.1    | 0.0909 | 0.8874   |
| 0.0036        | 23.0  | 230  | 0.4630          | 0.1034    | 0.15   | 0.1224 | 0.9063   |
| 0.003         | 24.0  | 240  | 0.5352          | 0.12      | 0.15   | 0.1333 | 0.9144   |
| 0.0028        | 25.0  | 250  | 0.5582          | 0.1111    | 0.1    | 0.1053 | 0.9107   |
| 0.0016        | 26.0  | 260  | 0.6038          | 0.075     | 0.15   | 0.1    | 0.9009   |
| 0.0024        | 27.0  | 270  | 0.5846          | 0.0909    | 0.1    | 0.0952 | 0.9124   |
| 0.0011        | 28.0  | 280  | 0.5600          | 0.125     | 0.15   | 0.1364 | 0.8993   |
| 0.0007        | 29.0  | 290  | 0.5614          | 0.0938    | 0.15   | 0.1154 | 0.8954   |
| 0.0006        | 30.0  | 300  | 0.5480          | 0.1176    | 0.1    | 0.1081 | 0.9129   |
| 0.006         | 31.0  | 310  | 0.5170          | 0.1176    | 0.2    | 0.1481 | 0.9039   |
| 0.0012        | 32.0  | 320  | 0.5699          | 0.0769    | 0.05   | 0.0606 | 0.8961   |
| 0.0004        | 33.0  | 330  | 0.6046          | 0.0476    | 0.05   | 0.0488 | 0.8928   |
| 0.0002        | 34.0  | 340  | 0.6708          | 0.0556    | 0.05   | 0.0526 | 0.8919   |
| 0.0001        | 35.0  | 350  | 0.7087          | 0.0435    | 0.05   | 0.0465 | 0.8948   |
| 0.0002        | 36.0  | 360  | 0.7225          | 0.05      | 0.05   | 0.0500 | 0.8976   |
| 0.0           | 37.0  | 370  | 0.7294          | 0.0435    | 0.05   | 0.0465 | 0.8985   |
| 0.0003        | 38.0  | 380  | 0.7182          | 0.0370    | 0.05   | 0.0426 | 0.9026   |
| 0.0002        | 39.0  | 390  | 0.7298          | 0.05      | 0.05   | 0.0500 | 0.9061   |
| 0.0003        | 40.0  | 400  | 0.7313          | 0.0588    | 0.05   | 0.0541 | 0.9068   |
| 0.0           | 41.0  | 410  | 0.7412          | 0.0526    | 0.05   | 0.0513 | 0.9068   |
| 0.0           | 42.0  | 420  | 0.7447          | 0.0556    | 0.05   | 0.0526 | 0.9068   |
| 0.0           | 43.0  | 430  | 0.7465          | 0.0588    | 0.05   | 0.0541 | 0.9076   |
| 0.0           | 44.0  | 440  | 0.7500          | 0.0455    | 0.05   | 0.0476 | 0.9070   |
| 0.0           | 45.0  | 450  | 0.7525          | 0.0435    | 0.05   | 0.0465 | 0.9065   |
| 0.0002        | 46.0  | 460  | 0.7540          | 0.0476    | 0.05   | 0.0488 | 0.9068   |
| 0.0003        | 47.0  | 470  | 0.7422          | 0.0455    | 0.05   | 0.0476 | 0.9068   |
| 0.0           | 48.0  | 480  | 0.7378          | 0.0435    | 0.05   | 0.0465 | 0.9070   |
| 0.0           | 49.0  | 490  | 0.7384          | 0.0417    | 0.05   | 0.0455 | 0.9068   |
| 0.0           | 50.0  | 500  | 0.7414          | 0.0455    | 0.05   | 0.0476 | 0.9070   |
| 0.0           | 51.0  | 510  | 0.7446          | 0.0455    | 0.05   | 0.0476 | 0.9070   |
| 0.0           | 52.0  | 520  | 0.7432          | 0.0385    | 0.05   | 0.0435 | 0.9061   |
| 0.0003        | 53.0  | 530  | 0.7474          | 0.0417    | 0.05   | 0.0455 | 0.9065   |
| 0.0002        | 54.0  | 540  | 0.7513          | 0.04      | 0.05   | 0.0444 | 0.9068   |
| 0.0           | 55.0  | 550  | 0.7505          | 0.0455    | 0.05   | 0.0476 | 0.9076   |
| 0.0003        | 56.0  | 560  | 0.7419          | 0.0417    | 0.05   | 0.0455 | 0.9072   |
| 0.0           | 57.0  | 570  | 0.7352          | 0.04      | 0.05   | 0.0444 | 0.9070   |
| 0.0           | 58.0  | 580  | 0.7330          | 0.04      | 0.05   | 0.0444 | 0.9068   |
| 0.0           | 59.0  | 590  | 0.7330          | 0.04      | 0.05   | 0.0444 | 0.9063   |
| 0.0           | 60.0  | 600  | 0.7343          | 0.04      | 0.05   | 0.0444 | 0.9061   |
| 0.0           | 61.0  | 610  | 0.7370          | 0.0385    | 0.05   | 0.0435 | 0.9063   |
| 0.0003        | 62.0  | 620  | 0.7303          | 0.04      | 0.05   | 0.0444 | 0.9063   |
| 0.0           | 63.0  | 630  | 0.7275          | 0.04      | 0.05   | 0.0444 | 0.9059   |
| 0.0           | 64.0  | 640  | 0.7283          | 0.04      | 0.05   | 0.0444 | 0.9057   |
| 0.0           | 65.0  | 650  | 0.7312          | 0.04      | 0.05   | 0.0444 | 0.9059   |
| 0.0002        | 66.0  | 660  | 0.7243          | 0.0345    | 0.05   | 0.0408 | 0.9074   |
| 0.0001        | 67.0  | 670  | 0.7195          | 0.05      | 0.05   | 0.0500 | 0.9081   |
| 0.0001        | 68.0  | 680  | 0.7350          | 0.0714    | 0.05   | 0.0588 | 0.9061   |
| 0.0001        | 69.0  | 690  | 0.7750          | 0.0625    | 0.05   | 0.0556 | 0.9061   |
| 0.0005        | 70.0  | 700  | 0.6559          | 0.0571    | 0.1    | 0.0727 | 0.9031   |
| 0.0003        | 71.0  | 710  | 0.6497          | 0.0385    | 0.05   | 0.0435 | 0.9131   |
| 0.0002        | 72.0  | 720  | 0.6621          | 0.0588    | 0.05   | 0.0541 | 0.9133   |
| 0.0007        | 73.0  | 730  | 0.6093          | 0.0741    | 0.1    | 0.0851 | 0.9126   |
| 0.0004        | 74.0  | 740  | 0.6184          | 0.0909    | 0.1    | 0.0952 | 0.9135   |
| 0.0005        | 75.0  | 750  | 0.5911          | 0.0952    | 0.1    | 0.0976 | 0.9142   |
| 0.0001        | 76.0  | 760  | 0.5567          | 0.0625    | 0.1    | 0.0769 | 0.9102   |
| 0.0002        | 77.0  | 770  | 0.5670          | 0.0571    | 0.1    | 0.0727 | 0.9096   |
| 0.0002        | 78.0  | 780  | 0.5940          | 0.0588    | 0.1    | 0.0741 | 0.9129   |
| 0.0001        | 79.0  | 790  | 0.6299          | 0.0455    | 0.05   | 0.0476 | 0.9139   |
| 0.0           | 80.0  | 800  | 0.6449          | 0.0455    | 0.05   | 0.0476 | 0.9135   |
| 0.0           | 81.0  | 810  | 0.6519          | 0.0417    | 0.05   | 0.0455 | 0.9131   |
| 0.0002        | 82.0  | 820  | 0.6378          | 0.0690    | 0.1    | 0.0816 | 0.9118   |
| 0.0           | 83.0  | 830  | 0.6267          | 0.0588    | 0.1    | 0.0741 | 0.9046   |
| 0.0004        | 84.0  | 840  | 0.6174          | 0.0625    | 0.1    | 0.0769 | 0.9065   |
| 0.0002        | 85.0  | 850  | 0.6174          | 0.0714    | 0.1    | 0.0833 | 0.9124   |
| 0.0001        | 86.0  | 860  | 0.6225          | 0.0909    | 0.1    | 0.0952 | 0.9135   |
| 0.0001        | 87.0  | 870  | 0.6384          | 0.0526    | 0.05   | 0.0513 | 0.9144   |
| 0.0           | 88.0  | 880  | 0.6604          | 0.0625    | 0.05   | 0.0556 | 0.9139   |
| 0.0           | 89.0  | 890  | 0.6694          | 0.0625    | 0.05   | 0.0556 | 0.9137   |
| 0.0           | 90.0  | 900  | 0.6711          | 0.0588    | 0.05   | 0.0541 | 0.9133   |
| 0.0001        | 91.0  | 910  | 0.6526          | 0.0435    | 0.05   | 0.0465 | 0.9124   |
| 0.0           | 92.0  | 920  | 0.6450          | 0.0417    | 0.05   | 0.0455 | 0.9124   |
| 0.0002        | 93.0  | 930  | 0.6504          | 0.0417    | 0.05   | 0.0455 | 0.9113   |
| 0.0           | 94.0  | 940  | 0.6711          | 0.0455    | 0.05   | 0.0476 | 0.9118   |
| 0.0           | 95.0  | 950  | 0.6789          | 0.0417    | 0.05   | 0.0455 | 0.9118   |
| 0.0           | 96.0  | 960  | 0.6828          | 0.0476    | 0.05   | 0.0488 | 0.9111   |
| 0.0           | 97.0  | 970  | 0.6863          | 0.0455    | 0.05   | 0.0476 | 0.9111   |
| 0.0001        | 98.0  | 980  | 0.6811          | 0.04      | 0.05   | 0.0444 | 0.9115   |
| 0.0           | 99.0  | 990  | 0.6787          | 0.0833    | 0.1    | 0.0909 | 0.9133   |
| 0.0001        | 100.0 | 1000 | 0.6914          | 0.0476    | 0.05   | 0.0488 | 0.9120   |
| 0.0           | 101.0 | 1010 | 0.7028          | 0.0588    | 0.05   | 0.0541 | 0.9118   |
| 0.0           | 102.0 | 1020 | 0.7089          | 0.0556    | 0.05   | 0.0526 | 0.9109   |
| 0.0           | 103.0 | 1030 | 0.7152          | 0.0588    | 0.05   | 0.0541 | 0.9111   |
| 0.0           | 104.0 | 1040 | 0.7151          | 0.0625    | 0.05   | 0.0556 | 0.9107   |
| 0.0           | 105.0 | 1050 | 0.7126          | 0.0556    | 0.05   | 0.0526 | 0.9105   |
| 0.0           | 106.0 | 1060 | 0.7065          | 0.0526    | 0.05   | 0.0513 | 0.9094   |
| 0.0002        | 107.0 | 1070 | 0.7154          | 0.05      | 0.05   | 0.0500 | 0.9089   |
| 0.0001        | 108.0 | 1080 | 0.6777          | 0.12      | 0.15   | 0.1333 | 0.9078   |
| 0.0           | 109.0 | 1090 | 0.6683          | 0.1       | 0.15   | 0.12   | 0.9074   |
| 0.0001        | 110.0 | 1100 | 0.6622          | 0.0909    | 0.15   | 0.1132 | 0.9070   |
| 0.0003        | 111.0 | 1110 | 0.6750          | 0.08      | 0.1    | 0.0889 | 0.9057   |
| 0.0001        | 112.0 | 1120 | 0.7000          | 0.1053    | 0.1    | 0.1026 | 0.9061   |
| 0.0001        | 113.0 | 1130 | 0.7136          | 0.1053    | 0.1    | 0.1026 | 0.9046   |
| 0.0001        | 114.0 | 1140 | 0.7234          | 0.1       | 0.1    | 0.1000 | 0.9037   |
| 0.0           | 115.0 | 1150 | 0.7643          | 0.0870    | 0.1    | 0.0930 | 0.8998   |
| 0.0001        | 116.0 | 1160 | 0.7801          | 0.0769    | 0.1    | 0.0870 | 0.9002   |
| 0.0           | 117.0 | 1170 | 0.7872          | 0.0769    | 0.1    | 0.0870 | 0.9009   |
| 0.0003        | 118.0 | 1180 | 0.7597          | 0.0690    | 0.1    | 0.0816 | 0.8983   |
| 0.0002        | 119.0 | 1190 | 0.7564          | 0.0606    | 0.1    | 0.0755 | 0.8989   |
| 0.0           | 120.0 | 1200 | 0.7558          | 0.0606    | 0.1    | 0.0755 | 0.8998   |
| 0.0           | 121.0 | 1210 | 0.7566          | 0.0625    | 0.1    | 0.0769 | 0.9002   |
| 0.0           | 122.0 | 1220 | 0.7579          | 0.0606    | 0.1    | 0.0755 | 0.8991   |
| 0.0           | 123.0 | 1230 | 0.7603          | 0.0606    | 0.1    | 0.0755 | 0.8989   |
| 0.0           | 124.0 | 1240 | 0.7626          | 0.0667    | 0.1    | 0.08   | 0.8980   |
| 0.0           | 125.0 | 1250 | 0.7645          | 0.0690    | 0.1    | 0.0816 | 0.8980   |
| 0.0           | 126.0 | 1260 | 0.7666          | 0.0625    | 0.1    | 0.0769 | 0.8978   |
| 0.0           | 127.0 | 1270 | 0.7689          | 0.0645    | 0.1    | 0.0784 | 0.8976   |
| 0.0           | 128.0 | 1280 | 0.7707          | 0.0645    | 0.1    | 0.0784 | 0.8974   |
| 0.0           | 129.0 | 1290 | 0.7718          | 0.0645    | 0.1    | 0.0784 | 0.8967   |
| 0.0           | 130.0 | 1300 | 0.7730          | 0.0606    | 0.1    | 0.0755 | 0.8976   |
| 0.0           | 131.0 | 1310 | 0.7738          | 0.0606    | 0.1    | 0.0755 | 0.8989   |
| 0.0003        | 132.0 | 1320 | 0.7744          | 0.0588    | 0.1    | 0.0741 | 0.9002   |
| 0.0           | 133.0 | 1330 | 0.7762          | 0.0606    | 0.1    | 0.0755 | 0.9013   |
| 0.0           | 134.0 | 1340 | 0.7784          | 0.0606    | 0.1    | 0.0755 | 0.9037   |
| 0.0           | 135.0 | 1350 | 0.7798          | 0.0667    | 0.1    | 0.08   | 0.9048   |
| 0.0002        | 136.0 | 1360 | 0.7357          | 0.0588    | 0.1    | 0.0741 | 0.9002   |
| 0.0002        | 137.0 | 1370 | 0.6891          | 0.08      | 0.1    | 0.0889 | 0.9      |
| 0.0001        | 138.0 | 1380 | 0.6732          | 0.0769    | 0.1    | 0.0870 | 0.9065   |
| 0.0001        | 139.0 | 1390 | 0.6808          | 0.0870    | 0.1    | 0.0930 | 0.9096   |
| 0.0           | 140.0 | 1400 | 0.6845          | 0.0833    | 0.1    | 0.0909 | 0.9098   |
| 0.0           | 141.0 | 1410 | 0.6880          | 0.0870    | 0.1    | 0.0930 | 0.9096   |
| 0.0           | 142.0 | 1420 | 0.6915          | 0.0870    | 0.1    | 0.0930 | 0.9096   |
| 0.0           | 143.0 | 1430 | 0.6945          | 0.08      | 0.1    | 0.0889 | 0.9096   |
| 0.0           | 144.0 | 1440 | 0.6966          | 0.0769    | 0.1    | 0.0870 | 0.9094   |
| 0.0           | 145.0 | 1450 | 0.6986          | 0.0909    | 0.1    | 0.0952 | 0.9109   |
| 0.0           | 146.0 | 1460 | 0.7015          | 0.0952    | 0.1    | 0.0976 | 0.9109   |
| 0.0           | 147.0 | 1470 | 0.7036          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 148.0 | 1480 | 0.7054          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 149.0 | 1490 | 0.7078          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 150.0 | 1500 | 0.7091          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 151.0 | 1510 | 0.7111          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 152.0 | 1520 | 0.7127          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 153.0 | 1530 | 0.7141          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 154.0 | 1540 | 0.7160          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 155.0 | 1550 | 0.7191          | 0.1053    | 0.1    | 0.1026 | 0.9109   |
| 0.0           | 156.0 | 1560 | 0.7205          | 0.1053    | 0.1    | 0.1026 | 0.9109   |
| 0.0           | 157.0 | 1570 | 0.7217          | 0.1053    | 0.1    | 0.1026 | 0.9109   |
| 0.0           | 158.0 | 1580 | 0.7225          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 159.0 | 1590 | 0.7231          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 160.0 | 1600 | 0.7238          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 161.0 | 1610 | 0.7245          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 162.0 | 1620 | 0.7252          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 163.0 | 1630 | 0.7258          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 164.0 | 1640 | 0.7261          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 165.0 | 1650 | 0.7266          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 166.0 | 1660 | 0.7273          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 167.0 | 1670 | 0.7278          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 168.0 | 1680 | 0.7286          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 169.0 | 1690 | 0.7295          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 170.0 | 1700 | 0.7303          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 171.0 | 1710 | 0.7310          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0           | 172.0 | 1720 | 0.7316          | 0.1       | 0.1    | 0.1000 | 0.9113   |
| 0.0002        | 173.0 | 1730 | 0.7248          | 0.1       | 0.1    | 0.1000 | 0.9107   |
| 0.0           | 174.0 | 1740 | 0.7180          | 0.0909    | 0.1    | 0.0952 | 0.9096   |
| 0.0003        | 175.0 | 1750 | 0.7154          | 0.0909    | 0.1    | 0.0952 | 0.9096   |
| 0.0           | 176.0 | 1760 | 0.7161          | 0.0909    | 0.1    | 0.0952 | 0.9094   |
| 0.0           | 177.0 | 1770 | 0.7251          | 0.0870    | 0.1    | 0.0930 | 0.9094   |
| 0.0           | 178.0 | 1780 | 0.7282          | 0.0870    | 0.1    | 0.0930 | 0.9094   |
| 0.0           | 179.0 | 1790 | 0.7297          | 0.0870    | 0.1    | 0.0930 | 0.9094   |
| 0.0           | 180.0 | 1800 | 0.7304          | 0.0870    | 0.1    | 0.0930 | 0.9094   |
| 0.0           | 181.0 | 1810 | 0.7308          | 0.0870    | 0.1    | 0.0930 | 0.9094   |
| 0.0           | 182.0 | 1820 | 0.7315          | 0.0870    | 0.1    | 0.0930 | 0.9094   |
| 0.0           | 183.0 | 1830 | 0.7334          | 0.0833    | 0.1    | 0.0909 | 0.9092   |
| 0.0           | 184.0 | 1840 | 0.7345          | 0.0833    | 0.1    | 0.0909 | 0.9092   |
| 0.0           | 185.0 | 1850 | 0.7349          | 0.0833    | 0.1    | 0.0909 | 0.9092   |
| 0.0           | 186.0 | 1860 | 0.7353          | 0.0833    | 0.1    | 0.0909 | 0.9092   |
| 0.0           | 187.0 | 1870 | 0.7356          | 0.0833    | 0.1    | 0.0909 | 0.9092   |
| 0.0           | 188.0 | 1880 | 0.7360          | 0.0833    | 0.1    | 0.0909 | 0.9092   |
| 0.0           | 189.0 | 1890 | 0.7365          | 0.0833    | 0.1    | 0.0909 | 0.9092   |
| 0.0           | 190.0 | 1900 | 0.7368          | 0.0833    | 0.1    | 0.0909 | 0.9092   |
| 0.0           | 191.0 | 1910 | 0.7370          | 0.0833    | 0.1    | 0.0909 | 0.9092   |
| 0.0           | 192.0 | 1920 | 0.7374          | 0.0833    | 0.1    | 0.0909 | 0.9092   |
| 0.0           | 193.0 | 1930 | 0.7375          | 0.0833    | 0.1    | 0.0909 | 0.9092   |
| 0.0           | 194.0 | 1940 | 0.7378          | 0.0833    | 0.1    | 0.0909 | 0.9092   |
| 0.0           | 195.0 | 1950 | 0.7379          | 0.0833    | 0.1    | 0.0909 | 0.9092   |
| 0.0           | 196.0 | 1960 | 0.7378          | 0.0833    | 0.1    | 0.0909 | 0.9092   |
| 0.0           | 197.0 | 1970 | 0.7381          | 0.0833    | 0.1    | 0.0909 | 0.9092   |
| 0.0           | 198.0 | 1980 | 0.7384          | 0.0833    | 0.1    | 0.0909 | 0.9092   |
| 0.0           | 199.0 | 1990 | 0.7385          | 0.0833    | 0.1    | 0.0909 | 0.9092   |
| 0.0           | 200.0 | 2000 | 0.7385          | 0.0833    | 0.1    | 0.0909 | 0.9092   |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "beto-finetuned-token-reqadjinsiders", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

beto-finetuned-token-reqadjinsiders
===================================


This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7385
* Precision: 0.0833
* Recall: 0.1
* F1: 0.0909
* Accuracy: 0.9092


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: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 200


### Training results



### Framework versions


* Transformers 4.31.0.dev0
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.31.0.dev0
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.31.0.dev0
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3