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alex2awesome
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source-affiliation-model

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roberta
Generated from Trainer
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metadata
license: mit
tags:
  - generated_from_trainer
metrics:
  - f1
model-index:
  - name: source-affiliation-model
    results: []

source-affiliation-model

This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 2.3321
  • F1: 0.5348

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: 5e-05
  • train_batch_size: 5
  • eval_batch_size: 5
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10.0

Training results

Training Loss Epoch Step Validation Loss F1
No log 0.12 100 1.4535 0.2435
No log 0.25 200 1.3128 0.3899
No log 0.37 300 1.2888 0.4413
No log 0.49 400 1.1560 0.4614
1.4848 0.62 500 1.0988 0.4477
1.4848 0.74 600 1.1211 0.4583
1.4848 0.86 700 1.1152 0.4693
1.4848 0.99 800 1.0176 0.5018
1.4848 1.11 900 1.0942 0.4774
1.1019 1.23 1000 1.1785 0.5119
1.1019 1.35 1100 1.0751 0.4797
1.1019 1.48 1200 1.0759 0.5206
1.1019 1.6 1300 1.0756 0.5231
1.1019 1.72 1400 1.1329 0.4547
0.9431 1.85 1500 1.0617 0.4852
0.9431 1.97 1600 1.1046 0.5254
0.9431 2.09 1700 1.2489 0.5069
0.9431 2.22 1800 1.2113 0.5363
0.9431 2.34 1900 1.1782 0.5546
0.7589 2.46 2000 1.0453 0.5862
0.7589 2.59 2100 1.0810 0.5223
0.7589 2.71 2200 1.1470 0.5872
0.7589 2.83 2300 1.1522 0.5553
0.7589 2.96 2400 1.0712 0.6273
0.6875 3.08 2500 1.3458 0.5768
0.6875 3.2 2600 1.7052 0.5491
0.6875 3.33 2700 1.5080 0.6582
0.6875 3.45 2800 1.5851 0.5965
0.6875 3.57 2900 1.4771 0.5691
0.5391 3.69 3000 1.6717 0.5350
0.5391 3.82 3100 1.5607 0.5448
0.5391 3.94 3200 1.5464 0.6062
0.5391 4.06 3300 1.7645 0.5755
0.5391 4.19 3400 1.6715 0.5504
0.4928 4.31 3500 1.7604 0.5626
0.4928 4.43 3600 1.8984 0.5142
0.4928 4.56 3700 1.8012 0.5763
0.4928 4.68 3800 1.7107 0.5671
0.4928 4.8 3900 1.7697 0.5598
0.4233 4.93 4000 1.6296 0.6084
0.4233 5.05 4100 2.0418 0.5343
0.4233 5.17 4200 1.8203 0.5526
0.4233 5.3 4300 1.9760 0.5292
0.4233 5.42 4400 2.0136 0.5153
0.2518 5.54 4500 2.0137 0.5121
0.2518 5.67 4600 2.0053 0.5257
0.2518 5.79 4700 1.9539 0.5423
0.2518 5.91 4800 2.0159 0.5686
0.2518 6.03 4900 2.0411 0.5817
0.2234 6.16 5000 2.0025 0.5780
0.2234 6.28 5100 2.1189 0.5413
0.2234 6.4 5200 2.1936 0.5628
0.2234 6.53 5300 2.1825 0.5210
0.2234 6.65 5400 2.0767 0.5471
0.1829 6.77 5500 1.9747 0.5587
0.1829 6.9 5600 2.1182 0.5847
0.1829 7.02 5700 2.1597 0.5437
0.1829 7.14 5800 2.0307 0.5629
0.1829 7.27 5900 2.0912 0.5450
0.1226 7.39 6000 2.2383 0.5379
0.1226 7.51 6100 2.2311 0.5834
0.1226 7.64 6200 2.2456 0.5438
0.1226 7.76 6300 2.2423 0.5860
0.1226 7.88 6400 2.2922 0.5245
0.0883 8.0 6500 2.3304 0.5650
0.0883 8.13 6600 2.3929 0.5288
0.0883 8.25 6700 2.3928 0.5344
0.0883 8.37 6800 2.3854 0.5266
0.0883 8.5 6900 2.4275 0.5339
0.044 8.62 7000 2.3929 0.5380
0.044 8.74 7100 2.3587 0.5339
0.044 8.87 7200 2.3372 0.5423
0.044 8.99 7300 2.3488 0.5424
0.044 9.11 7400 2.3543 0.5818
0.0558 9.24 7500 2.3397 0.5554
0.0558 9.36 7600 2.3255 0.5394
0.0558 9.48 7700 2.3184 0.5557
0.0558 9.61 7800 2.3293 0.5669
0.0558 9.73 7900 2.3358 0.5666
0.0323 9.85 8000 2.3307 0.5344
0.0323 9.98 8100 2.3321 0.5348

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.13.1
  • Tokenizers 0.13.3