dinhlnd1610's picture
End of training
aac3bc5
|
raw
history blame
2.94 kB
metadata
license: mit
base_model: xlnet-base-cased
tags:
  - generated_from_trainer
datasets:
  - tweet_sentiment_multilingual
metrics:
  - accuracy
model-index:
  - name: xlnet-finetuned-socialmediatweet
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: tweet_sentiment_multilingual
          type: tweet_sentiment_multilingual
          config: english
          split: validation
          args: english
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7129629850387573

xlnet-finetuned-socialmediatweet

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

  • Loss: 2.6923
  • Accuracy: 0.7130

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: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.0161 1.0 58 2.4538 0.6821
0.0416 2.0 116 2.3751 0.6821
0.0294 3.0 174 2.4929 0.7068
0.031 4.0 232 2.5655 0.7037
0.0422 5.0 290 3.0881 0.6605
0.0751 6.0 348 2.6787 0.6883
0.0264 7.0 406 2.5283 0.7006
0.0123 8.0 464 2.5634 0.7006
0.0277 9.0 522 2.7127 0.6852
0.0448 10.0 580 2.6113 0.6759
0.0261 11.0 638 2.6640 0.6759
0.0111 12.0 696 2.6089 0.6914
0.0239 13.0 754 2.5785 0.6975
0.0255 14.0 812 2.6923 0.7130
0.0242 15.0 870 2.4704 0.7068
0.0131 16.0 928 2.6724 0.6667
0.0059 17.0 986 2.5554 0.7068
0.0066 18.0 1044 2.6696 0.6698
0.001 19.0 1102 2.5653 0.6883
0.0026 20.0 1160 2.5846 0.6883

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0