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best_model-yelp_polarity-32-87

Text Classification
Transformers
PyTorch
albert
Generated from Trainer
Inference Endpoints
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metadata
license: apache-2.0
base_model: albert-base-v2
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: best_model-yelp_polarity-32-87
    results: []

best_model-yelp_polarity-32-87

This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5929
  • Accuracy: 0.9375

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: 1e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 150

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 2 0.4518 0.9531
No log 2.0 4 0.4575 0.9531
No log 3.0 6 0.4656 0.9531
No log 4.0 8 0.4755 0.9531
0.3146 5.0 10 0.5047 0.9375
0.3146 6.0 12 0.5491 0.9375
0.3146 7.0 14 0.5854 0.9375
0.3146 8.0 16 0.6060 0.9375
0.3146 9.0 18 0.6187 0.9375
0.2346 10.0 20 0.6304 0.9375
0.2346 11.0 22 0.6336 0.9375
0.2346 12.0 24 0.6359 0.9375
0.2346 13.0 26 0.6345 0.9375
0.2346 14.0 28 0.6333 0.9375
0.0633 15.0 30 0.6349 0.9375
0.0633 16.0 32 0.6359 0.9375
0.0633 17.0 34 0.6298 0.9375
0.0633 18.0 36 0.6191 0.9375
0.0633 19.0 38 0.6057 0.9375
0.0003 20.0 40 0.5963 0.9375
0.0003 21.0 42 0.5988 0.9375
0.0003 22.0 44 0.6050 0.9375
0.0003 23.0 46 0.6098 0.9375
0.0003 24.0 48 0.6134 0.9375
0.0 25.0 50 0.6160 0.9375
0.0 26.0 52 0.6177 0.9375
0.0 27.0 54 0.6187 0.9375
0.0 28.0 56 0.6190 0.9375
0.0 29.0 58 0.6189 0.9375
0.0 30.0 60 0.6186 0.9375
0.0 31.0 62 0.6179 0.9375
0.0 32.0 64 0.6172 0.9375
0.0 33.0 66 0.6161 0.9375
0.0 34.0 68 0.6151 0.9375
0.0 35.0 70 0.6140 0.9375
0.0 36.0 72 0.6128 0.9375
0.0 37.0 74 0.6116 0.9375
0.0 38.0 76 0.6104 0.9375
0.0 39.0 78 0.6091 0.9375
0.0 40.0 80 0.6079 0.9375
0.0 41.0 82 0.6066 0.9375
0.0 42.0 84 0.6054 0.9375
0.0 43.0 86 0.6041 0.9375
0.0 44.0 88 0.6029 0.9375
0.0 45.0 90 0.6018 0.9375
0.0 46.0 92 0.6006 0.9375
0.0 47.0 94 0.5996 0.9375
0.0 48.0 96 0.5986 0.9375
0.0 49.0 98 0.5977 0.9375
0.0 50.0 100 0.5968 0.9375
0.0 51.0 102 0.5959 0.9375
0.0 52.0 104 0.5951 0.9375
0.0 53.0 106 0.5943 0.9375
0.0 54.0 108 0.5937 0.9375
0.0 55.0 110 0.5931 0.9375
0.0 56.0 112 0.5925 0.9375
0.0 57.0 114 0.5918 0.9375
0.0 58.0 116 0.5912 0.9375
0.0 59.0 118 0.5906 0.9375
0.0 60.0 120 0.5899 0.9375
0.0 61.0 122 0.5894 0.9375
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0.0 63.0 126 0.5886 0.9375
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0.0 66.0 132 0.5874 0.9375
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0.0 69.0 138 0.5872 0.9375
0.0 70.0 140 0.5871 0.9375
0.0 71.0 142 0.5872 0.9375
0.0 72.0 144 0.5872 0.9375
0.0 73.0 146 0.5872 0.9375
0.0 74.0 148 0.5873 0.9375
0.0 75.0 150 0.5873 0.9375
0.0 76.0 152 0.5875 0.9375
0.0 77.0 154 0.5875 0.9375
0.0 78.0 156 0.5876 0.9375
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0.0 111.0 222 0.5870 0.9375
0.0 112.0 224 0.5870 0.9375
0.0 113.0 226 0.5872 0.9375
0.0 114.0 228 0.5877 0.9375
0.0 115.0 230 0.5881 0.9375
0.0 116.0 232 0.5885 0.9375
0.0 117.0 234 0.5888 0.9375
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0.0 121.0 242 0.5899 0.9375
0.0 122.0 244 0.5900 0.9375
0.0 123.0 246 0.5901 0.9375
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0.0 147.0 294 0.5926 0.9375
0.0 148.0 296 0.5927 0.9375
0.0 149.0 298 0.5929 0.9375
0.0 150.0 300 0.5929 0.9375

Framework versions

  • Transformers 4.32.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.4.0
  • Tokenizers 0.13.3