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scenario-TCR-xlmv-en-massive

Text Classification
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PyTorch
xlm-roberta
Generated from Trainer
Eval Results
Inference Endpoints
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scenario-TCR-xlmv-en-massive / README.md
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metadata
license: mit
base_model: facebook/xlm-v-base
tags:
  - generated_from_trainer
datasets:
  - massive
metrics:
  - accuracy
  - f1
model-index:
  - name: scenario-TCR_data-en-massive_all_1_1
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: massive
          type: massive
          config: all_1.1
          split: validation
          args: all_1.1
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7100778333960244
          - name: F1
            type: f1
            value: 0.6550778448597152

scenario-TCR_data-en-massive_all_1_1

This model is a fine-tuned version of facebook/xlm-v-base on the massive dataset. It achieves the following results on the evaluation set:

  • Loss: 2.3802
  • Accuracy: 0.7101
  • F1: 0.6551

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: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
No log 0.28 100 3.6542 0.0800 0.0085
No log 0.56 200 2.9766 0.3048 0.0953
No log 0.83 300 2.4835 0.3498 0.1168
No log 1.11 400 2.1305 0.4616 0.2154
2.7657 1.39 500 1.8889 0.5374 0.2791
2.7657 1.67 600 1.7326 0.5726 0.3208
2.7657 1.94 700 1.6536 0.5870 0.3726
2.7657 2.22 800 1.6709 0.5987 0.4014
2.7657 2.5 900 1.5460 0.6337 0.4720
1.1591 2.78 1000 1.5165 0.6434 0.4904
1.1591 3.06 1100 1.3861 0.6736 0.5237
1.1591 3.33 1200 1.3776 0.6739 0.5320
1.1591 3.61 1300 1.3753 0.6734 0.5521
1.1591 3.89 1400 1.4680 0.6624 0.5368
0.6194 4.17 1500 1.3899 0.6795 0.5520
0.6194 4.44 1600 1.5509 0.6640 0.5482
0.6194 4.72 1700 1.4034 0.6837 0.5764
0.6194 5.0 1800 1.4750 0.6739 0.5814
0.6194 5.28 1900 1.5321 0.6697 0.5761
0.3858 5.56 2000 1.5022 0.6822 0.5912
0.3858 5.83 2100 1.4612 0.6865 0.6016
0.3858 6.11 2200 1.4079 0.7034 0.6204
0.3858 6.39 2300 1.5165 0.6922 0.6296
0.3858 6.67 2400 1.6168 0.6736 0.6157
0.259 6.94 2500 1.5425 0.6948 0.6261
0.259 7.22 2600 1.6145 0.6796 0.6035
0.259 7.5 2700 1.5916 0.6824 0.6175
0.259 7.78 2800 1.5966 0.6977 0.6306
0.259 8.06 2900 1.4939 0.7125 0.6274
0.1759 8.33 3000 1.8425 0.6714 0.6170
0.1759 8.61 3100 1.6688 0.6923 0.6403
0.1759 8.89 3200 1.6218 0.6997 0.6220
0.1759 9.17 3300 1.7825 0.6829 0.6223
0.1759 9.44 3400 1.8706 0.6916 0.6294
0.1162 9.72 3500 1.8082 0.6884 0.6280
0.1162 10.0 3600 1.6708 0.7096 0.6338
0.1162 10.28 3700 1.7170 0.7100 0.6490
0.1162 10.56 3800 1.8575 0.6917 0.6264
0.1162 10.83 3900 1.8307 0.6959 0.6448
0.092 11.11 4000 1.9248 0.6958 0.6359
0.092 11.39 4100 1.7551 0.7162 0.6508
0.092 11.67 4200 1.8234 0.7072 0.6465
0.092 11.94 4300 2.1146 0.6790 0.6285
0.092 12.22 4400 1.9964 0.6909 0.6411
0.0582 12.5 4500 2.0290 0.6852 0.6313
0.0582 12.78 4600 2.0828 0.6838 0.6355
0.0582 13.06 4700 1.9272 0.7013 0.6312
0.0582 13.33 4800 1.9882 0.6959 0.6334
0.0582 13.61 4900 1.9552 0.7116 0.6511
0.0398 13.89 5000 2.0269 0.7060 0.6451
0.0398 14.17 5100 2.1377 0.6929 0.6414
0.0398 14.44 5200 2.1114 0.6880 0.6373
0.0398 14.72 5300 2.1517 0.6927 0.6438
0.0398 15.0 5400 2.2472 0.6921 0.6499
0.0311 15.28 5500 2.1801 0.6993 0.6557
0.0311 15.56 5600 2.1090 0.7020 0.6458
0.0311 15.83 5700 2.0049 0.7160 0.6590
0.0311 16.11 5800 2.2198 0.6959 0.6460
0.0311 16.39 5900 2.1074 0.7087 0.6519
0.0223 16.67 6000 2.0899 0.7096 0.6563
0.0223 16.94 6100 2.1736 0.7026 0.6546
0.0223 17.22 6200 2.1829 0.7004 0.6496
0.0223 17.5 6300 2.2041 0.6973 0.6450
0.0223 17.78 6400 2.1969 0.7074 0.6566
0.0178 18.06 6500 2.4021 0.6931 0.6515
0.0178 18.33 6600 2.2865 0.7092 0.6619
0.0178 18.61 6700 2.3086 0.7018 0.6504
0.0178 18.89 6800 2.2665 0.7054 0.6535
0.0178 19.17 6900 2.2723 0.7061 0.6525
0.0129 19.44 7000 2.2976 0.7030 0.6483
0.0129 19.72 7100 2.3634 0.7011 0.6514
0.0129 20.0 7200 2.3313 0.6971 0.6464
0.0129 20.28 7300 2.4373 0.6907 0.6439
0.0129 20.56 7400 2.2424 0.7139 0.6588
0.0125 20.83 7500 2.2329 0.7098 0.6547
0.0125 21.11 7600 2.2365 0.7107 0.6607
0.0125 21.39 7700 2.2925 0.7096 0.6593
0.0125 21.67 7800 2.3717 0.6998 0.6486
0.0125 21.94 7900 2.4211 0.6951 0.6479
0.0104 22.22 8000 2.3714 0.6978 0.6434
0.0104 22.5 8100 2.3995 0.7004 0.6503
0.0104 22.78 8200 2.3877 0.7044 0.6521
0.0104 23.06 8300 2.4957 0.6972 0.6482
0.0104 23.33 8400 2.2553 0.7180 0.6591
0.0061 23.61 8500 2.3877 0.7068 0.6560
0.0061 23.89 8600 2.4298 0.7036 0.6557
0.0061 24.17 8700 2.3903 0.7055 0.6516
0.0061 24.44 8800 2.3298 0.7065 0.6493
0.0061 24.72 8900 2.3245 0.7110 0.6535
0.0054 25.0 9000 2.3287 0.7086 0.6494
0.0054 25.28 9100 2.4519 0.6989 0.6427
0.0054 25.56 9200 2.4671 0.6988 0.6421
0.0054 25.83 9300 2.5166 0.6955 0.6447
0.0054 26.11 9400 2.4190 0.7056 0.6500
0.0029 26.39 9500 2.4361 0.7049 0.6511
0.0029 26.67 9600 2.4765 0.7029 0.6496
0.0029 26.94 9700 2.5246 0.6988 0.6460
0.0029 27.22 9800 2.4363 0.7051 0.6491
0.0029 27.5 9900 2.4066 0.7075 0.6514
0.0025 27.78 10000 2.3870 0.7092 0.6556
0.0025 28.06 10100 2.4028 0.7081 0.6539
0.0025 28.33 10200 2.3983 0.7080 0.6537
0.0025 28.61 10300 2.3876 0.7088 0.6552
0.0025 28.89 10400 2.4032 0.7080 0.6542
0.0025 29.17 10500 2.4138 0.7081 0.6544
0.0025 29.44 10600 2.3880 0.7098 0.6555
0.0025 29.72 10700 2.3801 0.7100 0.6552
0.0025 30.0 10800 2.3802 0.7101 0.6551

Framework versions

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