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finetuned-bert-categories-estimation

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metadata
tags:
  - generated_from_trainer
metrics:
  - f1
  - accuracy
model-index:
  - name: finetuned-bert-categories-estimation
    results: []

finetuned-bert-categories-estimation

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4354
  • F1: 0.9168
  • Accuracy: 0.9383

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: 32
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss F1 Accuracy
0.1375 0.15 100 0.4396 0.8593 0.9085
0.1136 0.29 200 0.4757 0.8533 0.8988
0.1273 0.44 300 0.4634 0.8637 0.9054
0.1202 0.59 400 0.4444 0.8638 0.9091
0.1372 0.73 500 0.4322 0.8708 0.9106
0.1598 0.88 600 0.4442 0.8734 0.9115
0.1918 1.03 700 0.4158 0.8715 0.9107
0.1404 1.17 800 0.4295 0.8772 0.9115
0.1479 1.32 900 0.4024 0.8849 0.9190
0.1374 1.47 1000 0.4125 0.8798 0.9170
0.1504 1.62 1100 0.3967 0.8857 0.9201
0.1204 1.76 1200 0.3960 0.8860 0.9201
0.1449 1.91 1300 0.4093 0.8890 0.9177
0.1208 2.06 1400 0.4064 0.8841 0.9203
0.0884 2.2 1500 0.4128 0.8881 0.9203
0.1073 2.35 1600 0.3934 0.8940 0.9243
0.0937 2.5 1700 0.4158 0.8888 0.9196
0.0931 2.64 1800 0.4028 0.8903 0.9230
0.0967 2.79 1900 0.4015 0.9001 0.9269
0.094 2.94 2000 0.4116 0.8970 0.9258
0.074 3.08 2100 0.4183 0.8978 0.9251
0.0593 3.23 2200 0.4177 0.8971 0.9262
0.085 3.38 2300 0.3933 0.9092 0.9306
0.0764 3.52 2400 0.4245 0.9008 0.9276
0.0849 3.67 2500 0.4044 0.8983 0.9273
0.0833 3.82 2600 0.4089 0.9021 0.9286
0.1134 3.96 2700 0.4212 0.8989 0.9251
0.0572 4.11 2800 0.4295 0.9056 0.9275
0.0651 4.26 2900 0.4111 0.9010 0.9267
0.0524 4.41 3000 0.3951 0.9064 0.9309
0.0572 4.55 3100 0.4091 0.9030 0.9282
0.0585 4.7 3200 0.4222 0.9003 0.9275
0.0615 4.85 3300 0.4134 0.9056 0.9311
0.0663 4.99 3400 0.4200 0.9046 0.9293
0.028 5.14 3500 0.4131 0.9057 0.9331
0.0196 5.29 3600 0.4436 0.9017 0.9293
0.0237 5.43 3700 0.4316 0.9054 0.9309
0.0278 5.58 3800 0.4364 0.9017 0.9280
0.0352 5.73 3900 0.4294 0.9021 0.9284
0.0547 5.87 4000 0.4202 0.9098 0.9320
0.0512 6.02 4100 0.4280 0.9083 0.9311
0.0201 6.17 4200 0.4336 0.9099 0.9311
0.0192 6.31 4300 0.4329 0.9078 0.9330
0.0167 6.46 4400 0.4318 0.9091 0.9331
0.0305 6.61 4500 0.4288 0.9085 0.9333
0.0178 6.75 4600 0.4269 0.9111 0.9337
0.0268 6.9 4700 0.4267 0.9114 0.9337
0.0207 7.05 4800 0.4281 0.9115 0.9344
0.0116 7.2 4900 0.4329 0.9111 0.9348
0.0104 7.34 5000 0.4445 0.9089 0.9335
0.0149 7.49 5100 0.4394 0.9114 0.9343
0.0084 7.64 5200 0.4367 0.9145 0.9350
0.0151 7.78 5300 0.4460 0.9087 0.9319
0.012 7.93 5400 0.4368 0.9130 0.9354
0.0083 8.08 5500 0.4354 0.9122 0.9350
0.0089 8.22 5600 0.4319 0.9120 0.9344
0.0063 8.37 5700 0.4304 0.9139 0.9359
0.0089 8.52 5800 0.4297 0.9136 0.9352
0.0081 8.66 5900 0.4348 0.9128 0.9348
0.0084 8.81 6000 0.4361 0.9126 0.9354
0.0051 8.96 6100 0.4373 0.9140 0.9366
0.0049 9.1 6200 0.4374 0.9167 0.9376
0.0049 9.25 6300 0.4349 0.9170 0.9377
0.004 9.4 6400 0.4358 0.9174 0.9385
0.0046 9.54 6500 0.4352 0.9175 0.9385
0.0108 9.69 6600 0.4355 0.9171 0.9381
0.0039 9.84 6700 0.4357 0.9168 0.9383
0.0053 9.99 6800 0.4354 0.9168 0.9383

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0