jayanta / twitter-roberta-base-sentiment-sentiment-memes-30epcohs

huggingface.co
Total runs: 16
24-hour runs: 0
7-day runs: 5
30-day runs: 4
Model's Last Updated: September 23 2022
text-classification

Introduction of twitter-roberta-base-sentiment-sentiment-memes-30epcohs

Model Details of twitter-roberta-base-sentiment-sentiment-memes-30epcohs

twitter-roberta-base-sentiment-sentiment-memes-30epcohs

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.3027
  • Accuracy: 0.8517
  • Precision: 0.8536
  • Recall: 0.8517
  • F1: 0.8523
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: 2e-05
  • 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: 30
Training results
Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
0.2504 1.0 2147 0.7129 0.8087 0.8112 0.8087 0.8036
0.2449 2.0 4294 0.7500 0.8229 0.8279 0.8229 0.8240
0.2652 3.0 6441 0.7460 0.8181 0.8185 0.8181 0.8149
0.2585 4.0 8588 0.7906 0.8155 0.8152 0.8155 0.8153
0.2534 5.0 10735 0.8178 0.8061 0.8180 0.8061 0.8080
0.2498 6.0 12882 0.8139 0.8166 0.8163 0.8166 0.8164
0.2825 7.0 15029 0.7494 0.8155 0.8210 0.8155 0.8168
0.2459 8.0 17176 0.8870 0.8061 0.8122 0.8061 0.8075
0.2303 9.0 19323 0.8699 0.7987 0.8060 0.7987 0.8003
0.2425 10.0 21470 0.8043 0.8244 0.8275 0.8244 0.8253
0.2143 11.0 23617 0.9163 0.8208 0.8251 0.8208 0.8219
0.2054 12.0 25764 0.8330 0.8239 0.8258 0.8239 0.8245
0.208 13.0 27911 1.0673 0.8134 0.8216 0.8134 0.8150
0.1668 14.0 30058 0.9071 0.8270 0.8276 0.8270 0.8273
0.1571 15.0 32205 0.9294 0.8339 0.8352 0.8339 0.8344
0.1857 16.0 34352 0.9909 0.8354 0.8350 0.8354 0.8352
0.1476 17.0 36499 0.9747 0.8433 0.8436 0.8433 0.8434
0.1341 18.0 38646 0.9372 0.8422 0.8415 0.8422 0.8415
0.1181 19.0 40793 1.0301 0.8433 0.8443 0.8433 0.8437
0.1192 20.0 42940 1.1332 0.8407 0.8415 0.8407 0.8410
0.0983 21.0 45087 1.2002 0.8428 0.8498 0.8428 0.8440
0.0951 22.0 47234 1.2141 0.8475 0.8504 0.8475 0.8483
0.0784 23.0 49381 1.1652 0.8407 0.8453 0.8407 0.8417
0.0623 24.0 51528 1.1730 0.8417 0.8443 0.8417 0.8425
0.054 25.0 53675 1.2900 0.8454 0.8496 0.8454 0.8464
0.0584 26.0 55822 1.2831 0.8480 0.8497 0.8480 0.8486
0.0531 27.0 57969 1.3043 0.8506 0.8524 0.8506 0.8512
0.0522 28.0 60116 1.2891 0.8527 0.8554 0.8527 0.8534
0.037 29.0 62263 1.3077 0.8538 0.8559 0.8538 0.8544
0.038 30.0 64410 1.3027 0.8517 0.8536 0.8517 0.8523
Framework versions
  • Transformers 4.13.0.dev0
  • Pytorch 1.11.0+cu102
  • Datasets 1.15.2.dev0
  • Tokenizers 0.10.1

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