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xlm-roberta-base-finetuned-ner

Token Classification PyTorch Transformers
mit xlm-roberta generated_from_trainer AutoTrain Compatible
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xlm-roberta-base-finetuned-ner

This model is a fine-tuned version of xlm-roberta-base on the conll2003 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1202
  • Precision: 0.9447
  • Recall: 0.9536
  • F1: 0.9492
  • Accuracy: 0.9883

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: 5
  • eval_batch_size: 5
  • 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 Precision Recall F1 Accuracy
0.1023 1.0 2809 0.0724 0.9338 0.9363 0.9351 0.9850
0.0596 2.0 5618 0.0760 0.9295 0.9359 0.9327 0.9848
0.0406 3.0 8427 0.0740 0.9346 0.9410 0.9378 0.9863
0.0365 4.0 11236 0.0676 0.9368 0.9490 0.9428 0.9870
0.0279 5.0 14045 0.0737 0.9453 0.9476 0.9464 0.9877
0.0147 6.0 16854 0.0812 0.9413 0.9515 0.9464 0.9878
0.0138 7.0 19663 0.0893 0.9425 0.9525 0.9475 0.9876
0.0158 8.0 22472 0.1066 0.9362 0.9464 0.9412 0.9862
0.0092 9.0 25281 0.1026 0.9391 0.9511 0.9451 0.9869
0.0073 10.0 28090 0.1001 0.9442 0.9503 0.9472 0.9879
0.0069 11.0 30899 0.1103 0.9399 0.9511 0.9455 0.9871
0.0073 12.0 33708 0.1170 0.9383 0.9481 0.9432 0.9876
0.0054 13.0 36517 0.1068 0.9407 0.9491 0.9448 0.9875
0.0048 14.0 39326 0.1096 0.9438 0.9518 0.9477 0.9879
0.0042 15.0 42135 0.1187 0.9442 0.9523 0.9483 0.9884
0.0037 16.0 44944 0.1162 0.9384 0.9521 0.9452 0.9875
0.0039 17.0 47753 0.1046 0.9435 0.9477 0.9456 0.9878
0.0025 18.0 50562 0.1063 0.9501 0.9549 0.9525 0.9889
0.0021 19.0 53371 0.0992 0.9533 0.9572 0.9553 0.9895
0.0019 20.0 56180 0.1216 0.9404 0.9524 0.9464 0.9876
0.0021 21.0 58989 0.1080 0.9430 0.9478 0.9454 0.9880
0.0032 22.0 61798 0.1109 0.9436 0.9512 0.9474 0.9881
0.0115 23.0 64607 0.1161 0.9412 0.9475 0.9443 0.9874
0.001 24.0 67416 0.1216 0.9446 0.9518 0.9481 0.9882
0.0004 25.0 70225 0.1145 0.9478 0.9527 0.9503 0.9888
0.0005 26.0 73034 0.1217 0.9479 0.9531 0.9505 0.9887
0.0007 27.0 75843 0.1199 0.9452 0.9561 0.9506 0.9887
0.0053 28.0 78652 0.1187 0.9440 0.9510 0.9475 0.9881
0.0014 29.0 81461 0.1207 0.9461 0.9540 0.9500 0.9884
0.0023 30.0 84270 0.1202 0.9447 0.9536 0.9492 0.9883

Framework versions

  • Transformers 4.9.2
  • Pytorch 1.9.0+cu102
  • Datasets 1.11.0
  • Tokenizers 0.10.3
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Dataset used to train vitvit/xlm-roberta-base-finetuned-ner