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hubert-classifier-aug

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metadata
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: hubert-classifier
    results: []

hubert-classifier

This model is a fine-tuned version of facebook/hubert-base-ls960 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 2.4637
  • Accuracy: 0.5230
  • Precision: 0.4945
  • Recall: 0.5230
  • F1: 0.4700
  • Binary: 0.6634

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: 3e-05
  • train_batch_size: 64
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 Binary
No log 1.72 50 4.2179 0.0484 0.0065 0.0484 0.0105 0.3058
No log 3.45 100 3.8319 0.1017 0.0846 0.1017 0.0618 0.3634
No log 5.17 150 3.5448 0.1864 0.1327 0.1864 0.1311 0.4274
No log 6.9 200 3.3129 0.2470 0.2063 0.2470 0.1855 0.4671
No log 8.62 250 3.1207 0.3123 0.3090 0.3123 0.2599 0.5150
No log 10.34 300 2.9535 0.3826 0.3524 0.3826 0.3277 0.5644
No log 12.07 350 2.8121 0.4310 0.3894 0.4310 0.3695 0.5983
No log 13.79 400 2.6726 0.4431 0.3939 0.4431 0.3775 0.6075
No log 15.52 450 2.5597 0.4818 0.4413 0.4818 0.4206 0.6370
3.4474 17.24 500 2.4637 0.5230 0.4945 0.5230 0.4700 0.6634
3.4474 18.97 550 2.3747 0.5400 0.5111 0.5400 0.4920 0.6760
3.4474 20.69 600 2.3113 0.5545 0.5212 0.5545 0.5067 0.6872
3.4474 22.41 650 2.2492 0.5714 0.5475 0.5714 0.5274 0.7007
3.4474 24.14 700 2.2053 0.5738 0.5511 0.5738 0.5336 0.7015
3.4474 25.86 750 2.1757 0.5714 0.5477 0.5714 0.5283 0.7015
3.4474 27.59 800 2.1491 0.5908 0.5574 0.5908 0.5468 0.7140
3.4474 29.31 850 2.1403 0.5932 0.5625 0.5932 0.5506 0.7167

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.3.0
  • Datasets 2.19.1
  • Tokenizers 0.15.1