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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - emotion
metrics:
  - accuracy
base_model: albert-base-v2
model-index:
  - name: fine-tuned-albert-tweets
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: emotion
          type: emotion
          config: split
          split: validation
          args: split
        metrics:
          - type: accuracy
            value: 0.9305
            name: Accuracy

fine-tuned-albert-tweets

This model is a fine-tuned version of albert-base-v2 on the emotion dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1757
  • Accuracy: 0.9305

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: 2

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.3202 1.0 1000 0.2518 0.912
0.1537 2.0 2000 0.1757 0.9305

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2