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XLMRoBERTa_Lexical_Dataset45K

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xlm-roberta
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metadata
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: XLMRoBERTa_Lexical_Dataset45K
    results: []

XLMRoBERTa_Lexical_Dataset45K

This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4964
  • Accuracy: 0.8870
  • F1: 0.8870

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: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
No log 0.2841 200 0.4614 0.7988 0.7972
No log 0.5682 400 0.3716 0.8339 0.8335
No log 0.8523 600 0.3603 0.8456 0.8474
0.455 1.1364 800 0.3298 0.8618 0.8612
0.455 1.4205 1000 0.3466 0.8621 0.8598
0.455 1.7045 1200 0.3051 0.8697 0.8712
0.455 1.9886 1400 0.2924 0.8773 0.8774
0.3111 2.2727 1600 0.2764 0.8847 0.8853
0.3111 2.5568 1800 0.2648 0.8865 0.8866
0.3111 2.8409 2000 0.2571 0.8890 0.8894
0.2536 3.125 2200 0.2745 0.8901 0.8898
0.2536 3.4091 2400 0.2801 0.8729 0.8747
0.2536 3.6932 2600 0.2863 0.8892 0.8885
0.2536 3.9773 2800 0.2587 0.8909 0.8902
0.2249 4.2614 3000 0.2728 0.8905 0.8903
0.2249 4.5455 3200 0.2782 0.8873 0.8882
0.2249 4.8295 3400 0.2696 0.8900 0.8908
0.1984 5.1136 3600 0.3226 0.8896 0.8890
0.1984 5.3977 3800 0.2821 0.8921 0.8923
0.1984 5.6818 4000 0.3206 0.8818 0.8830
0.1984 5.9659 4200 0.2800 0.8938 0.8936
0.178 6.25 4400 0.2892 0.8938 0.8938
0.178 6.5341 4600 0.3262 0.8875 0.8880
0.178 6.8182 4800 0.3199 0.8876 0.8874
0.1556 7.1023 5000 0.3237 0.8889 0.8892
0.1556 7.3864 5200 0.3540 0.8883 0.8880
0.1556 7.6705 5400 0.3361 0.8925 0.8925
0.1556 7.9545 5600 0.3369 0.8868 0.8877
0.1344 8.2386 5800 0.3449 0.8889 0.8887
0.1344 8.5227 6000 0.3813 0.8888 0.8884
0.1344 8.8068 6200 0.3463 0.8908 0.8910
0.1203 9.0909 6400 0.3781 0.8886 0.8893
0.1203 9.375 6600 0.3825 0.8897 0.8897
0.1203 9.6591 6800 0.3842 0.8890 0.8895
0.1203 9.9432 7000 0.3894 0.8885 0.8893
0.1069 10.2273 7200 0.4063 0.8903 0.8900
0.1069 10.5114 7400 0.3976 0.8908 0.8912
0.1069 10.7955 7600 0.4013 0.8892 0.8893
0.0942 11.0795 7800 0.4663 0.8888 0.8886
0.0942 11.3636 8000 0.4394 0.8867 0.8871
0.0942 11.6477 8200 0.4770 0.8852 0.8859
0.0942 11.9318 8400 0.4278 0.8881 0.8881
0.0827 12.2159 8600 0.4431 0.8874 0.8877
0.0827 12.5 8800 0.4553 0.8851 0.8856
0.0827 12.7841 9000 0.4546 0.8859 0.8861
0.0772 13.0682 9200 0.4571 0.8883 0.8881
0.0772 13.3523 9400 0.4655 0.8872 0.8870
0.0772 13.6364 9600 0.4700 0.8872 0.8876
0.0772 13.9205 9800 0.4817 0.8863 0.8867
0.0692 14.2045 10000 0.4838 0.8864 0.8868
0.0692 14.4886 10200 0.4825 0.8868 0.8871
0.0692 14.7727 10400 0.4964 0.8870 0.8870

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.1.2
  • Datasets 2.20.0
  • Tokenizers 0.19.1