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muhtasham
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tajroberto-ner

Token Classification
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roberta
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tajroberto-ner

This model is a fine-tuned version of muhtasham/RoBERTa-tg on the wikiann dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9408
  • Precision: 0.3155
  • Recall: 0.5673
  • F1: 0.4055
  • Accuracy: 0.8360

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 200

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 2.0 50 0.7710 0.0532 0.1827 0.0824 0.6933
No log 4.0 100 0.5901 0.0847 0.25 0.1265 0.7825
No log 6.0 150 0.5226 0.2087 0.4615 0.2874 0.8186
No log 8.0 200 0.5041 0.2585 0.5096 0.3430 0.8449
No log 10.0 250 0.5592 0.2819 0.5096 0.3630 0.8499
No log 12.0 300 0.5725 0.3032 0.5481 0.3904 0.8558
No log 14.0 350 0.6433 0.3122 0.5673 0.4027 0.8508
No log 16.0 400 0.6744 0.3543 0.5962 0.4444 0.8553
No log 18.0 450 0.7617 0.3353 0.5577 0.4188 0.8335
0.2508 20.0 500 0.7608 0.3262 0.5865 0.4192 0.8419
0.2508 22.0 550 0.8483 0.3224 0.5673 0.4111 0.8494
0.2508 24.0 600 0.8370 0.3275 0.5385 0.4073 0.8439
0.2508 26.0 650 0.8652 0.3410 0.5673 0.4260 0.8394
0.2508 28.0 700 0.9441 0.3409 0.5769 0.4286 0.8216
0.2508 30.0 750 0.9228 0.3333 0.5577 0.4173 0.8439
0.2508 32.0 800 0.9175 0.3430 0.5673 0.4275 0.8355
0.2508 34.0 850 0.9603 0.3073 0.5288 0.3887 0.8340
0.2508 36.0 900 0.9417 0.3240 0.5577 0.4099 0.8370
0.2508 38.0 950 0.9408 0.3155 0.5673 0.4055 0.8360

Framework versions

  • Transformers 4.21.2
  • Pytorch 1.12.1+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1
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Dataset used to train muhtasham/tajroberto-ner

Collection including muhtasham/tajroberto-ner

Evaluation results

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