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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - amazon_reviews_multi
metrics:
  - accuracy
  - f1
base_model: distilbert-base-multilingual-cased
model-index:
  - name: distilbert-base-multilingual-cased-sentiment-2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: amazon_reviews_multi
          type: amazon_reviews_multi
          args: all_languages
        metrics:
          - type: accuracy
            value: 0.7475666666666667
            name: Accuracy
          - type: f1
            value: 0.7475666666666667
            name: F1

distilbert-base-multilingual-cased-sentiment-2

This model is a fine-tuned version of distilbert-base-multilingual-cased on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6067
  • Accuracy: 0.7476
  • F1: 0.7476

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: 0.00024
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 33
  • distributed_type: sagemaker_data_parallel
  • num_devices: 8
  • total_train_batch_size: 128
  • total_eval_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 3
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.6885 0.53 5000 0.6532 0.7217 0.7217
0.6411 1.07 10000 0.6348 0.7319 0.7319
0.6057 1.6 15000 0.6186 0.7387 0.7387
0.5844 2.13 20000 0.6236 0.7449 0.7449
0.549 2.67 25000 0.6067 0.7476 0.7476

Framework versions

  • Transformers 4.12.3
  • Pytorch 1.9.1
  • Datasets 1.15.1
  • Tokenizers 0.10.3