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ellis-v3-emotion-leadership

Text Classification
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Safetensors
distilbert
Generated from Trainer
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metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: ellis-v3-emotion-leadership
    results: []

NOTA: Este modelo será utilizado para comparação com a Versão 2.0. Poderá ser excluido após esta validação/comparação

ellis-v3-emotion-leadership

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 2.0202
  • Accuracy: 0.8402

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 70

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6209 1.0 1479 0.5094 0.8014
0.4893 2.0 2958 0.4642 0.8166
0.4244 3.0 4437 0.4640 0.8284
0.3607 4.0 5916 0.4596 0.8333
0.3021 5.0 7395 0.4892 0.8322
0.2391 6.0 8874 0.5455 0.8288
0.2066 7.0 10353 0.6553 0.8288
0.1669 8.0 11832 0.6856 0.8387
0.1515 9.0 13311 0.8654 0.8280
0.1154 10.0 14790 0.8985 0.8322
0.094 11.0 16269 1.1159 0.8280
0.0822 12.0 17748 1.1954 0.8227
0.082 13.0 19227 1.2213 0.8341
0.0656 14.0 20706 1.2533 0.8375
0.0592 15.0 22185 1.3723 0.8284
0.0539 16.0 23664 1.4376 0.8326
0.0533 17.0 25143 1.4746 0.8291
0.044 18.0 26622 1.4234 0.8288
0.0411 19.0 28101 1.4971 0.8253
0.0343 20.0 29580 1.5132 0.8284
0.0359 21.0 31059 1.5020 0.8360
0.0309 22.0 32538 1.6418 0.8356
0.0416 23.0 34017 1.4984 0.8303
0.0314 24.0 35496 1.5713 0.8341
0.0316 25.0 36975 1.5679 0.8352
0.0281 26.0 38454 1.6399 0.8311
0.0179 27.0 39933 1.7032 0.8231
0.0326 28.0 41412 1.6551 0.8330
0.0178 29.0 42891 1.7136 0.8284
0.0149 30.0 44370 1.7317 0.8288
0.0211 31.0 45849 1.6790 0.8314
0.0221 32.0 47328 1.7909 0.8280
0.0179 33.0 48807 1.8027 0.8314
0.022 34.0 50286 1.7754 0.8299
0.0198 35.0 51765 1.7498 0.8295
0.0124 36.0 53244 1.8098 0.8356
0.0123 37.0 54723 1.8535 0.8261
0.0103 38.0 56202 1.8827 0.8345
0.0145 39.0 57681 1.8882 0.8303
0.0162 40.0 59160 1.8174 0.8326
0.0103 41.0 60639 1.8350 0.8368
0.0103 42.0 62118 1.7853 0.8390
0.0136 43.0 63597 1.7032 0.8368
0.0099 44.0 65076 1.8274 0.8318
0.0074 45.0 66555 1.8598 0.8333
0.0108 46.0 68034 1.7978 0.8413
0.0063 47.0 69513 1.8116 0.8364
0.0112 48.0 70992 1.8066 0.8356
0.0038 49.0 72471 1.9092 0.8352
0.005 50.0 73950 1.9159 0.8356
0.0035 51.0 75429 1.8669 0.8379
0.0067 52.0 76908 1.9222 0.8333
0.0049 53.0 78387 1.8417 0.8398
0.0034 54.0 79866 2.0452 0.8311
0.0056 55.0 81345 1.9375 0.8349
0.0014 56.0 82824 1.9941 0.8322
0.0004 57.0 84303 2.0133 0.8349
0.0017 58.0 85782 2.0038 0.8356
0.0009 59.0 87261 2.0347 0.8356
0.0015 60.0 88740 1.9901 0.8368
0.0014 61.0 90219 2.0233 0.8368
0.0022 62.0 91698 2.0148 0.8356
0.0012 63.0 93177 1.9823 0.8383
0.0014 64.0 94656 2.0099 0.8368
0.0034 65.0 96135 1.9925 0.8402
0.0009 66.0 97614 2.0088 0.8390
0.0006 67.0 99093 2.0141 0.8394
0.0 68.0 100572 2.0199 0.8417
0.0012 69.0 102051 2.0187 0.8394
0.0 70.0 103530 2.0202 0.8402

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.1.0
  • Datasets 2.18.0
  • Tokenizers 0.15.2