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scenario-NON-KD-PR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_

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PyTorch
Safetensors
deberta-v2
Generated from Trainer
Inference Endpoints
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metadata
base_model: microsoft/mdeberta-v3-base
datasets:
  - tweet_sentiment_multilingual
library_name: transformers
license: mit
metrics:
  - accuracy
  - f1
tags:
  - generated_from_trainer
model-index:
  - name: >-
      scenario-NON-KD-PR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_
    results: []

scenario-NON-KD-PR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_

This model is a fine-tuned version of microsoft/mdeberta-v3-base on the tweet_sentiment_multilingual dataset. It achieves the following results on the evaluation set:

  • Loss: 4.7108
  • Accuracy: 0.5475
  • F1: 0.5469

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: 66
  • 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
1.0791 1.0870 500 1.0368 0.4660 0.4346
0.9866 2.1739 1000 1.0697 0.5116 0.4871
0.891 3.2609 1500 1.0182 0.5351 0.5315
0.7817 4.3478 2000 1.0793 0.5382 0.5268
0.6754 5.4348 2500 1.2254 0.5440 0.5347
0.5735 6.5217 3000 1.3490 0.5490 0.5419
0.4804 7.6087 3500 1.4240 0.5374 0.5365
0.402 8.6957 4000 1.6744 0.5409 0.5346
0.3338 9.7826 4500 1.8045 0.5293 0.5303
0.2826 10.8696 5000 1.8731 0.5340 0.5340
0.239 11.9565 5500 2.0811 0.5336 0.5331
0.1932 13.0435 6000 2.5003 0.5374 0.5358
0.172 14.1304 6500 2.3698 0.5374 0.5364
0.1508 15.2174 7000 2.9410 0.5502 0.5455
0.1364 16.3043 7500 2.9157 0.5463 0.5472
0.1274 17.3913 8000 2.8807 0.5417 0.5329
0.1187 18.4783 8500 3.1515 0.5332 0.5328
0.1021 19.5652 9000 3.1270 0.5355 0.5363
0.0998 20.6522 9500 3.1811 0.5521 0.5512
0.0919 21.7391 10000 3.0586 0.5409 0.5327
0.0904 22.8261 10500 3.1029 0.5382 0.5382
0.0785 23.9130 11000 3.3520 0.5405 0.5389
0.0695 25.0 11500 2.8631 0.5475 0.5443
0.0683 26.0870 12000 3.3984 0.5467 0.5462
0.0634 27.1739 12500 3.3375 0.5521 0.5520
0.0554 28.2609 13000 3.5643 0.5444 0.5442
0.0521 29.3478 13500 3.6555 0.5386 0.5373
0.0468 30.4348 14000 3.8146 0.5498 0.5477
0.0475 31.5217 14500 3.9862 0.5370 0.5374
0.0423 32.6087 15000 3.9440 0.5413 0.5377
0.0417 33.6957 15500 3.9646 0.5405 0.5409
0.0408 34.7826 16000 3.8754 0.5424 0.5433
0.0314 35.8696 16500 4.2460 0.5413 0.5394
0.0344 36.9565 17000 4.2120 0.5455 0.5444
0.0296 38.0435 17500 4.4753 0.5448 0.5451
0.0308 39.1304 18000 4.1944 0.5494 0.5491
0.0225 40.2174 18500 4.4062 0.5486 0.5466
0.0284 41.3043 19000 4.1900 0.5444 0.5428
0.0191 42.3913 19500 4.5725 0.5444 0.5441
0.0202 43.4783 20000 4.5546 0.5502 0.5492
0.019 44.5652 20500 4.6947 0.5463 0.5465
0.02 45.6522 21000 4.4766 0.5471 0.5462
0.0182 46.7391 21500 4.4498 0.5490 0.5480
0.0131 47.8261 22000 4.5762 0.5490 0.5484
0.0105 48.9130 22500 4.7128 0.5467 0.5464
0.015 50.0 23000 4.7108 0.5475 0.5469

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.1.1+cu121
  • Datasets 2.14.5
  • Tokenizers 0.19.1