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scenario-TCR-XLMV-XCOPA-5_data-xcopa_all

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xlm-roberta
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scenario-TCR-XLMV-XCOPA-5_data-xcopa_all / README.md
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metadata
license: mit
base_model: facebook/xlm-v-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: scenario-TCR-XLMV-XCOPA-5_data-xcopa_all
    results: []

scenario-TCR-XLMV-XCOPA-5_data-xcopa_all

This model is a fine-tuned version of facebook/xlm-v-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6931
  • Accuracy: 0.505
  • F1: 0.4396

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: 214
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 500

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
No log 0.38 5 0.6937 0.5158 0.4808
No log 0.77 10 0.6929 0.4917 0.4777
No log 1.15 15 0.6933 0.5025 0.4919
No log 1.54 20 0.6945 0.5125 0.4734
No log 1.92 25 0.6931 0.4767 0.4442
No log 2.31 30 0.6931 0.5192 0.4898
No log 2.69 35 0.6931 0.5042 0.4644
No log 3.08 40 0.6931 0.5183 0.5085
No log 3.46 45 0.6931 0.5083 0.4896
No log 3.85 50 0.6931 0.5158 0.5004
No log 4.23 55 0.6931 0.5292 0.5031
No log 4.62 60 0.6931 0.5233 0.5077
No log 5.0 65 0.6931 0.5292 0.5083
No log 5.38 70 0.6931 0.5 0.4643
No log 5.77 75 0.6931 0.4983 0.4837
No log 6.15 80 0.6932 0.4708 0.4385
No log 6.54 85 0.6932 0.475 0.4375
No log 6.92 90 0.6932 0.4717 0.4379
No log 7.31 95 0.6932 0.4925 0.4518
No log 7.69 100 0.6932 0.4758 0.4448
No log 8.08 105 0.6932 0.4908 0.4617
No log 8.46 110 0.6931 0.5017 0.4613
No log 8.85 115 0.6931 0.4875 0.4591
No log 9.23 120 0.6931 0.4783 0.4371
No log 9.62 125 0.6932 0.4533 0.4164
No log 10.0 130 0.6932 0.475 0.4335
No log 10.38 135 0.6931 0.5242 0.4870
No log 10.77 140 0.6931 0.5258 0.4969
No log 11.15 145 0.6931 0.4808 0.4452
No log 11.54 150 0.6932 0.4408 0.3993
No log 11.92 155 0.6932 0.4375 0.3968
No log 12.31 160 0.6932 0.4325 0.3925
No log 12.69 165 0.6932 0.435 0.4084
No log 13.08 170 0.6932 0.4825 0.4557
No log 13.46 175 0.6931 0.4892 0.4541
No log 13.85 180 0.6931 0.5 0.4828
No log 14.23 185 0.6931 0.5325 0.5109
No log 14.62 190 0.6931 0.5367 0.5071
No log 15.0 195 0.6932 0.47 0.4291
No log 15.38 200 0.6932 0.4483 0.4193
No log 15.77 205 0.6932 0.4325 0.4052
No log 16.15 210 0.6932 0.47 0.4411
No log 16.54 215 0.6932 0.47 0.4441
No log 16.92 220 0.6932 0.4567 0.4210
No log 17.31 225 0.6932 0.4408 0.4067
No log 17.69 230 0.6932 0.44 0.4032
No log 18.08 235 0.6932 0.455 0.4233
No log 18.46 240 0.6932 0.4558 0.4297
No log 18.85 245 0.6932 0.4542 0.4239
No log 19.23 250 0.6932 0.4783 0.4431
No log 19.62 255 0.6931 0.4908 0.4664
No log 20.0 260 0.6932 0.4825 0.4567
No log 20.38 265 0.6932 0.4775 0.4581
No log 20.77 270 0.6946 0.495 0.4580
No log 21.15 275 0.6931 0.5208 0.4806
No log 21.54 280 0.6931 0.5 0.4652
No log 21.92 285 0.6931 0.5067 0.4426
No log 22.31 290 0.6931 0.4908 0.3944
No log 22.69 295 0.6931 0.5317 0.4458
No log 23.08 300 0.6931 0.4933 0.4109
No log 23.46 305 0.6931 0.5025 0.4176
No log 23.85 310 0.6931 0.5033 0.4601
No log 24.23 315 0.6931 0.5017 0.4305
No log 24.62 320 0.6931 0.4883 0.4408
No log 25.0 325 0.6931 0.5267 0.4799
No log 25.38 330 0.6931 0.5083 0.4370
No log 25.77 335 0.6931 0.505 0.4396

Framework versions

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