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scenario-KD-PO-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all_alpha

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PyTorch
xlm-roberta
Generated from Trainer
Inference Endpoints
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scenario-KD-PO-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all_alpha / README.md
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metadata
license: mit
base_model: haryoaw/scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_a
tags:
  - generated_from_trainer
datasets:
  - tweet_sentiment_multilingual
metrics:
  - accuracy
  - f1
model-index:
  - name: >-
      scenario-KD-PO-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all_alpha
    results: []

scenario-KD-PO-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all_alpha

This model is a fine-tuned version of haryoaw/scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_a on the tweet_sentiment_multilingual dataset. It achieves the following results on the evaluation set:

  • Loss: 3.4750
  • Accuracy: 0.5637
  • F1: 0.5640

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: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 2222
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
4.905 1.09 500 4.5461 0.4363 0.4113
4.0059 2.17 1000 3.6093 0.5058 0.5040
3.4109 3.26 1500 3.4190 0.5208 0.5131
3.0676 4.35 2000 3.2675 0.5490 0.5477
2.7673 5.43 2500 3.2746 0.5467 0.5412
2.5421 6.52 3000 3.1951 0.5475 0.5367
2.3609 7.61 3500 3.3137 0.5432 0.5410
2.1176 8.7 4000 3.5963 0.5451 0.5303
1.9583 9.78 4500 3.5109 0.5571 0.5583
1.8268 10.87 5000 3.3664 0.5471 0.5477
1.7388 11.96 5500 3.3858 0.5517 0.5528
1.5976 13.04 6000 3.4404 0.5617 0.5577
1.4912 14.13 6500 3.3307 0.5586 0.5585
1.4157 15.22 7000 3.5579 0.5432 0.5355
1.3536 16.3 7500 3.3542 0.5617 0.5603
1.2883 17.39 8000 3.6026 0.5571 0.5543
1.2443 18.48 8500 3.6866 0.5478 0.5458
1.1637 19.57 9000 3.6125 0.5536 0.5547
1.1391 20.65 9500 3.5456 0.5613 0.5574
1.1029 21.74 10000 3.4366 0.5513 0.5526
1.0417 22.83 10500 3.6791 0.5586 0.5585
1.0169 23.91 11000 3.6637 0.5656 0.5607
1.0107 25.0 11500 3.5452 0.5575 0.5578
0.9502 26.09 12000 3.4362 0.5748 0.5742
0.9455 27.17 12500 3.4865 0.5694 0.5703
0.9194 28.26 13000 3.4523 0.5737 0.5716
0.9053 29.35 13500 3.5411 0.5586 0.5572
0.8737 30.43 14000 3.6550 0.5586 0.5586
0.865 31.52 14500 3.5079 0.5594 0.5611
0.8444 32.61 15000 3.4885 0.5509 0.5526
0.8343 33.7 15500 3.5705 0.5710 0.5698
0.8122 34.78 16000 3.4910 0.5521 0.5519
0.8161 35.87 16500 3.5302 0.5559 0.5563
0.7923 36.96 17000 3.5031 0.5656 0.5632
0.7824 38.04 17500 3.4182 0.5594 0.5592
0.7658 39.13 18000 3.5265 0.5594 0.5586
0.7588 40.22 18500 3.4465 0.5706 0.5711
0.7541 41.3 19000 3.4879 0.5540 0.5534
0.7488 42.39 19500 3.4246 0.5687 0.5693
0.7412 43.48 20000 3.4806 0.5745 0.5750
0.7314 44.57 20500 3.5638 0.5590 0.5586
0.7283 45.65 21000 3.4212 0.5664 0.5667
0.7179 46.74 21500 3.4444 0.5556 0.5560
0.7168 47.83 22000 3.4104 0.5602 0.5606
0.7161 48.91 22500 3.3766 0.5667 0.5676
0.7052 50.0 23000 3.4750 0.5637 0.5640

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.1.1+cu121
  • Datasets 2.14.5
  • Tokenizers 0.13.3