IIIT-L / albert-base-v2-finetuned-TRAC-DS

huggingface.co
Total runs: 10
24-hour runs: 0
7-day runs: -11
30-day runs: -21
Model's Last Updated: September 08 2022
text-classification

Introduction of albert-base-v2-finetuned-TRAC-DS

Model Details of albert-base-v2-finetuned-TRAC-DS

albert-base-v2-finetuned-TRAC-DS

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: 0.8271
  • Accuracy: 0.6315
  • Precision: 0.6206
  • Recall: 0.6201
  • F1: 0.6147
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: 5.919508251872584e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 43
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 4
Training results
Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
1.0373 1.0 612 1.1241 0.3627 0.5914 0.3618 0.2414
1.0617 2.0 1224 1.1039 0.3350 0.2781 0.3354 0.1740
0.9791 3.0 1836 0.8365 0.5989 0.6192 0.5887 0.5883
0.798 3.99 2448 0.8271 0.6315 0.6206 0.6201 0.6147
Framework versions
  • Transformers 4.21.3
  • Pytorch 1.12.1+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1

Runs of IIIT-L albert-base-v2-finetuned-TRAC-DS on huggingface.co

10
Total runs
0
24-hour runs
-3
3-day runs
-11
7-day runs
-21
30-day runs

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