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scenario-KD-PO-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all55

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PyTorch
deberta-v2
Generated from Trainer
Inference Endpoints
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scenario-KD-PO-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-PO-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all55
    results: []

scenario-KD-PO-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: 3.2137
  • Accuracy: 0.6150
  • F1: 0.6155

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
4.3541 1.09 500 3.1863 0.5721 0.5729
3.0722 2.17 1000 3.2433 0.5845 0.5841
2.1971 3.26 1500 3.4852 0.5907 0.5891
1.6717 4.35 2000 3.3784 0.5841 0.5859
1.3144 5.43 2500 3.7616 0.5864 0.5846
1.1448 6.52 3000 3.4276 0.5779 0.5777
0.9875 7.61 3500 3.3781 0.5953 0.5946
0.8762 8.7 4000 3.1471 0.5829 0.5843
0.7963 9.78 4500 3.3721 0.5752 0.5756
0.7369 10.87 5000 3.5433 0.5837 0.5839
0.6786 11.96 5500 3.3185 0.5783 0.5793
0.6271 13.04 6000 3.1903 0.5903 0.5869
0.5889 14.13 6500 3.3031 0.5895 0.5899
0.5569 15.22 7000 3.2597 0.5995 0.5987
0.5229 16.3 7500 3.1515 0.5883 0.5893
0.4985 17.39 8000 3.1954 0.5992 0.5997
0.4751 18.48 8500 3.2670 0.5988 0.5990
0.4643 19.57 9000 3.1884 0.5961 0.5948
0.4434 20.65 9500 3.2196 0.5984 0.5972
0.4381 21.74 10000 3.2211 0.5922 0.5934
0.4193 22.83 10500 3.1172 0.5992 0.5990
0.4113 23.91 11000 3.3000 0.6107 0.6114
0.4008 25.0 11500 3.1179 0.6015 0.6024
0.3914 26.09 12000 3.1496 0.5926 0.5925
0.3861 27.17 12500 3.2896 0.5934 0.5942
0.3768 28.26 13000 3.1737 0.6065 0.6076
0.3767 29.35 13500 3.2205 0.6038 0.6034
0.3687 30.43 14000 3.3223 0.6049 0.6046
0.3663 31.52 14500 3.0866 0.6015 0.6021
0.3542 32.61 15000 3.1709 0.6107 0.6119
0.3549 33.7 15500 3.2819 0.5965 0.5976
0.3476 34.78 16000 3.2686 0.5995 0.6002
0.3452 35.87 16500 3.2558 0.6015 0.6018
0.346 36.96 17000 3.1651 0.5953 0.5962
0.3402 38.04 17500 3.1907 0.6084 0.6086
0.3385 39.13 18000 3.1962 0.6096 0.6098
0.3389 40.22 18500 3.1716 0.6088 0.6091
0.3322 41.3 19000 3.1679 0.6092 0.6096
0.3288 42.39 19500 3.2305 0.6046 0.6044
0.333 43.48 20000 3.2468 0.6011 0.6016
0.33 44.57 20500 3.1011 0.6177 0.6180
0.3322 45.65 21000 3.2199 0.5980 0.5984
0.3267 46.74 21500 3.2237 0.6130 0.6132
0.3268 47.83 22000 3.2034 0.6038 0.6042
0.3238 48.91 22500 3.1129 0.6103 0.6106
0.324 50.0 23000 3.2137 0.6150 0.6155

Framework versions

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