Hugging Face's logo

DimasikKurd
/
rubert-base-cased_pos

Token Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
rubert-base-cased_pos / README.md
DimasikKurd's picture
DimasikKurd
Training complete
0a4624e verified
preview code
|
raw
history blame
8.07 kB
metadata
base_model: DeepPavlov/rubert-base-cased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: rubert-base-cased_pos
    results: []

rubert-base-cased_pos

This model is a fine-tuned version of DeepPavlov/rubert-base-cased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4707
  • Precision: 0.5814
  • Recall: 0.5824
  • F1: 0.5819
  • Accuracy: 0.9021

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: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.11 50 0.6626 0.0119 0.0019 0.0033 0.7605
No log 2.22 100 0.5508 0.0187 0.0193 0.0190 0.7994
No log 3.33 150 0.4129 0.1039 0.1139 0.1087 0.8451
No log 4.44 200 0.3683 0.1473 0.1448 0.1461 0.8555
No log 5.56 250 0.3231 0.2346 0.2934 0.2607 0.8687
No log 6.67 300 0.3154 0.3034 0.4498 0.3624 0.8752
No log 7.78 350 0.2665 0.3306 0.4595 0.3845 0.8931
No log 8.89 400 0.2724 0.3532 0.5734 0.4371 0.8929
No log 10.0 450 0.2694 0.4179 0.6042 0.4941 0.8997
0.3748 11.11 500 0.2984 0.4609 0.5347 0.4951 0.9051
0.3748 12.22 550 0.2823 0.4063 0.6448 0.4985 0.8926
0.3748 13.33 600 0.2894 0.4514 0.5463 0.4943 0.9022
0.3748 14.44 650 0.3150 0.5043 0.5676 0.5341 0.9043
0.3748 15.56 700 0.3608 0.4828 0.5965 0.5337 0.9018
0.3748 16.67 750 0.4073 0.4824 0.6332 0.5476 0.9003
0.3748 17.78 800 0.4018 0.5271 0.5811 0.5528 0.9083
0.3748 18.89 850 0.4261 0.528 0.6371 0.5774 0.9047
0.3748 20.0 900 0.4229 0.5187 0.6429 0.5741 0.9049
0.3748 21.11 950 0.4429 0.4511 0.6506 0.5328 0.8942
0.0787 22.22 1000 0.3901 0.5289 0.6004 0.5624 0.9102
0.0787 23.33 1050 0.3880 0.5 0.5965 0.5440 0.9122
0.0787 24.44 1100 0.3993 0.5462 0.6042 0.5738 0.9109
0.0787 25.56 1150 0.5128 0.5087 0.5097 0.5092 0.9010
0.0787 26.67 1200 0.3716 0.5492 0.6139 0.5798 0.9144
0.0787 27.78 1250 0.4023 0.5087 0.6197 0.5587 0.9093
0.0787 28.89 1300 0.4473 0.5461 0.6178 0.5797 0.9106
0.0787 30.0 1350 0.4449 0.5739 0.5772 0.5756 0.9067
0.0787 31.11 1400 0.5749 0.5466 0.5656 0.5560 0.9025
0.0787 32.22 1450 0.4480 0.5571 0.6120 0.5833 0.9125
0.0352 33.33 1500 0.4459 0.4877 0.6120 0.5428 0.9004
0.0352 34.44 1550 0.4265 0.5554 0.6293 0.5900 0.9148
0.0352 35.56 1600 0.3678 0.5983 0.5347 0.5647 0.9031
0.0352 36.67 1650 0.4182 0.5777 0.6100 0.5934 0.9120
0.0352 37.78 1700 0.4270 0.5767 0.6390 0.6062 0.9146
0.0352 38.89 1750 0.4614 0.5283 0.6486 0.5823 0.9045
0.0352 40.0 1800 0.4962 0.5553 0.5714 0.5633 0.9124
0.0352 41.11 1850 0.4498 0.5981 0.6178 0.6078 0.9169
0.0352 42.22 1900 0.4311 0.4920 0.6564 0.5624 0.9009
0.0352 43.33 1950 0.5744 0.5065 0.6023 0.5503 0.8935
0.0269 44.44 2000 0.4810 0.5325 0.5849 0.5575 0.9093
0.0269 45.56 2050 0.5074 0.5918 0.5541 0.5723 0.9064
0.0269 46.67 2100 0.5291 0.5081 0.6622 0.5750 0.8938
0.0269 47.78 2150 0.4815 0.5904 0.6429 0.6155 0.9092
0.0269 48.89 2200 0.5512 0.6294 0.5869 0.6074 0.9126
0.0269 50.0 2250 0.4686 0.5876 0.6023 0.5949 0.9094
0.0269 51.11 2300 0.4961 0.5942 0.5965 0.5954 0.9135
0.0269 52.22 2350 0.5033 0.5471 0.6390 0.5895 0.9046
0.0269 53.33 2400 0.5583 0.6308 0.5212 0.5708 0.9021
0.0269 54.44 2450 0.4128 0.5904 0.5927 0.5915 0.9003
0.0268 55.56 2500 0.4908 0.5219 0.5985 0.5576 0.9059
0.0268 56.67 2550 0.4669 0.5068 0.6506 0.5697 0.9004
0.0268 57.78 2600 0.4738 0.5692 0.6351 0.6004 0.9004
0.0268 58.89 2650 0.4933 0.6197 0.6197 0.6197 0.9185
0.0268 60.0 2700 0.4623 0.6060 0.6236 0.6147 0.9141
0.0268 61.11 2750 0.4472 0.5981 0.6178 0.6078 0.9163
0.0268 62.22 2800 0.4988 0.6410 0.6274 0.6341 0.9132
0.0268 63.33 2850 0.4900 0.6176 0.5830 0.5998 0.9065
0.0268 64.44 2900 0.4380 0.6410 0.6100 0.6251 0.9143
0.0268 65.56 2950 0.5097 0.6115 0.6564 0.6331 0.9130
0.0251 66.67 3000 0.3672 0.5845 0.6409 0.6114 0.9088
0.0251 67.78 3050 0.4630 0.5213 0.6371 0.5734 0.9106
0.0251 68.89 3100 0.4418 0.6375 0.6042 0.6204 0.9134
0.0251 70.0 3150 0.4648 0.5705 0.6718 0.6170 0.9122
0.0251 71.11 3200 0.4314 0.6388 0.5907 0.6138 0.9138
0.0251 72.22 3250 0.4000 0.5575 0.6178 0.5861 0.9121
0.0251 73.33 3300 0.3548 0.5039 0.6216 0.5566 0.9145
0.0251 74.44 3350 0.4886 0.6616 0.5849 0.6209 0.9124
0.0251 75.56 3400 0.5781 0.4402 0.6602 0.5282 0.8803
0.0251 76.67 3450 0.3379 0.5704 0.6178 0.5931 0.9088
0.0271 77.78 3500 0.4527 0.5303 0.6429 0.5812 0.9016
0.0271 78.89 3550 0.5293 0.6211 0.5792 0.5994 0.9085
0.0271 80.0 3600 0.6486 0.3684 0.6351 0.4663 0.8263

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.2
  • Datasets 2.1.0
  • Tokenizers 0.15.2