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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: 0.4181
  • Precision: 0.6464
  • Recall: 0.4904
  • F1: 0.5577
  • Accuracy: 0.9009
  • It just needs more training time

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
0.9474 1.0 2809 0.9105 0.0 0.0 0.0 0.7879
0.7728 2.0 5618 0.8002 0.0 0.0 0.0 0.7879
0.7209 3.0 8427 0.7329 0.1818 0.0002 0.0004 0.7881
0.6666 4.0 11236 0.6824 0.27 0.0050 0.0099 0.7903
0.6372 5.0 14045 0.6416 0.3302 0.0261 0.0484 0.7988
0.5982 6.0 16854 0.6084 0.4188 0.0686 0.1179 0.8128
0.5812 7.0 19663 0.5800 0.4799 0.1152 0.1858 0.8266
0.5684 8.0 22472 0.5569 0.5255 0.1647 0.2508 0.8380
0.5389 9.0 25281 0.5375 0.5564 0.2128 0.3078 0.8482
0.5307 10.0 28090 0.5205 0.5749 0.2550 0.3533 0.8567
0.5106 11.0 30899 0.5064 0.5916 0.2916 0.3906 0.8636
0.4921 12.0 33708 0.4938 0.6033 0.3236 0.4212 0.8698
0.4967 13.0 36517 0.4825 0.6106 0.3544 0.4485 0.8758
0.4707 14.0 39326 0.4733 0.6199 0.3753 0.4676 0.8798
0.4704 15.0 42135 0.4654 0.6246 0.3927 0.4823 0.8830
0.4654 16.0 44944 0.4574 0.6285 0.4159 0.5006 0.8871
0.4314 17.0 47753 0.4514 0.6321 0.4240 0.5075 0.8887
0.47 18.0 50562 0.4459 0.6358 0.4380 0.5187 0.8911
0.4486 19.0 53371 0.4410 0.6399 0.4480 0.5271 0.8929
0.4411 20.0 56180 0.4367 0.6413 0.4561 0.5331 0.8944
0.4333 21.0 58989 0.4328 0.6411 0.4644 0.5386 0.8959
0.4402 22.0 61798 0.4295 0.6425 0.4687 0.5420 0.8968
0.4287 23.0 64607 0.4268 0.6442 0.4735 0.5458 0.8978
0.4336 24.0 67416 0.4245 0.6441 0.4771 0.5482 0.8985
0.4243 25.0 70225 0.4224 0.6454 0.4817 0.5517 0.8993
0.4153 26.0 73034 0.4209 0.6469 0.4846 0.5541 0.8998
0.4286 27.0 75843 0.4197 0.6467 0.4865 0.5553 0.9002
0.436 28.0 78652 0.4188 0.6466 0.4887 0.5566 0.9006
0.427 29.0 81461 0.4183 0.6465 0.4900 0.5575 0.9008
0.4317 30.0 84270 0.4181 0.6464 0.4904 0.5577 0.9009

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/XLMRFineTuneonEnglishNERFrozenBase