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scenario-KD-PR-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all55

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PyTorch
deberta-v2
Generated from Trainer
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scenario-KD-PR-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all55 / README.md
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metadata
license: mit
base_model: haryoaw/scenario-MDBT-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all
tags:
  - generated_from_trainer
datasets:
  - tweet_sentiment_multilingual
metrics:
  - accuracy
  - f1
model-index:
  - name: >-
      scenario-KD-PR-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all55
    results: []

scenario-KD-PR-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all55

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

  • Loss: 1.2180
  • Accuracy: 0.5999
  • F1: 0.6008

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: 55
  • 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
1.2423 1.09 500 1.2214 0.4842 0.4591
1.1465 2.17 1000 1.2081 0.5498 0.5406
1.089 3.26 1500 1.2345 0.5540 0.5476
1.043 4.35 2000 1.2340 0.5756 0.5777
1.01 5.43 2500 1.2397 0.5706 0.5717
0.9787 6.52 3000 1.2536 0.5718 0.5723
0.9656 7.61 3500 1.2564 0.5579 0.5603
0.9505 8.7 4000 1.2641 0.5644 0.5660
0.9432 9.78 4500 1.2385 0.5880 0.5876
0.9304 10.87 5000 1.2612 0.5864 0.5862
0.9245 11.96 5500 1.2567 0.5748 0.5728
0.9189 13.04 6000 1.2463 0.5745 0.5745
0.9131 14.13 6500 1.2599 0.5729 0.5738
0.9098 15.22 7000 1.2614 0.5706 0.5704
0.9052 16.3 7500 1.2468 0.5741 0.5748
0.9013 17.39 8000 1.2550 0.5756 0.5775
0.8972 18.48 8500 1.2661 0.5733 0.5743
0.8972 19.57 9000 1.2506 0.5783 0.5780
0.8912 20.65 9500 1.2519 0.5737 0.5752
0.8903 21.74 10000 1.2313 0.5795 0.5782
0.8868 22.83 10500 1.2384 0.5895 0.5896
0.8847 23.91 11000 1.2474 0.5752 0.5736
0.8834 25.0 11500 1.2458 0.5791 0.5795
0.8815 26.09 12000 1.2548 0.5748 0.5739
0.8794 27.17 12500 1.2378 0.5864 0.5857
0.8791 28.26 13000 1.2327 0.5968 0.5953
0.8749 29.35 13500 1.2249 0.5949 0.5935
0.8748 30.43 14000 1.2309 0.5938 0.5905
0.8734 31.52 14500 1.2242 0.5880 0.5885
0.872 32.61 15000 1.2372 0.5841 0.5856
0.8712 33.7 15500 1.2394 0.5783 0.5800
0.87 34.78 16000 1.2363 0.5922 0.5921
0.8692 35.87 16500 1.2375 0.5903 0.5916
0.8677 36.96 17000 1.2341 0.5968 0.5951
0.8672 38.04 17500 1.2227 0.6038 0.6013
0.8657 39.13 18000 1.2250 0.5899 0.5904
0.865 40.22 18500 1.2275 0.5949 0.5952
0.865 41.3 19000 1.2196 0.5953 0.5958
0.864 42.39 19500 1.2375 0.5818 0.5815
0.8636 43.48 20000 1.2373 0.5849 0.5856
0.8635 44.57 20500 1.2292 0.5930 0.5940
0.8622 45.65 21000 1.2243 0.5903 0.5914
0.8619 46.74 21500 1.2198 0.5984 0.5992
0.8608 47.83 22000 1.2175 0.6046 0.6054
0.8621 48.91 22500 1.2179 0.5995 0.6004
0.8606 50.0 23000 1.2180 0.5999 0.6008

Framework versions

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