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Distilbert-uncased-AS

Este es un modelo de finetuning de distilbert-base-uncased sobre un dataset propios de tweets. Se logra una error cuadrático bajo lo cual quiere decir que los valores predichos son muy cercanos a los observables o gold.

  • Loss: 0.3510
  • Rmse: 0.2543

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: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Rmse
0.2091 1.0 642 0.1933 0.3052
0.1334 2.0 1284 0.1909 0.2481
0.0684 3.0 1926 0.2617 0.2466
0.0355 4.0 2568 0.3113 0.2513
0.0116 5.0 3210 0.3510 0.2543

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.18.0
  • Tokenizers 0.19.1
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Finetuned from