README.md

metadata

license: apache-2.0
base_model: albert/albert-base-v2
tags:
  - generated_from_trainer
datasets:
  - emotion
metrics:
  - accuracy
  - f1
model-index:
  - name: albert-base-v2-finetuned-emotion
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: emotion
          type: emotion
          config: split
          split: validation
          args: split
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.912
          - name: F1
            type: f1
            value: 0.911766000939379

albert-base-v2-finetuned-emotion

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

• Loss: 0.2451
• Accuracy: 0.912
• F1: 0.9118

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: 64
• eval_batch_size: 64
• 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	F1
0.9011	1.0	250	0.4077	0.877	0.8776
0.2633	2.0	500	0.2451	0.912	0.9118

Framework versions

• Transformers 4.41.0
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1