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gyr66/RoBERTa-ext-large-crf-chinese-finetuned-ner-v2

Model

should probably proofread and complete it, then remove this comment. -->

1

4 commits

1 linked in READMEs

updated Jan 9, 2024

See the code

README

RoBERTa-ext-large-crf-chinese-finetuned-ner

This model is a fine-tuned version of gyr66/RoBERTa-ext-large-chinese-finetuned-ner on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5907
  • Precision: 0.7278
  • Recall: 0.75
  • F1: 0.7387
  • Accuracy: 0.9629

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: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training LossEpochStepValidation LossPrecisionRecallF1Accuracy
0.00611.05030.67390.67470.74570.70840.9608
0.00782.010060.63430.70830.75180.72940.9622
0.00723.015090.62370.68670.76210.72240.9607
0.00524.020120.59290.71360.76160.73680.9635
0.00315.025150.59070.72780.750.73870.9629
0.00146.030180.60800.71720.75580.73600.9636
0.0017.035210.61790.71980.75860.73870.9637
0.00058.040240.62080.72110.75180.73610.9632
0.00049.045270.61690.72710.74870.73780.9636
0.000210.050300.62020.72660.74950.73790.9636

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0
bert
custom_code
generated_from_trainer
safetensors
token-classification
transformers

Contributors

gyr66

4 commits

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Model

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1 linked in READMEs

updated Jan 9, 2024

See the code

README

RoBERTa-ext-large-crf-chinese-finetuned-ner

This model is a fine-tuned version of gyr66/RoBERTa-ext-large-chinese-finetuned-ner on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5907
  • Precision: 0.7278
  • Recall: 0.75
  • F1: 0.7387
  • Accuracy: 0.9629

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: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training LossEpochStepValidation LossPrecisionRecallF1Accuracy
0.00611.05030.67390.67470.74570.70840.9608
0.00782.010060.63430.70830.75180.72940.9622
0.00723.015090.62370.68670.76210.72240.9607
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0.00315.025150.59070.72780.750.73870.9629
0.00146.030180.60800.71720.75580.73600.9636
0.0017.035210.61790.71980.75860.73870.9637
0.00058.040240.62080.72110.75180.73610.9632
0.00049.045270.61690.72710.74870.73780.9636
0.000210.050300.62020.72660.74950.73790.9636

Framework versions

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  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0
bert
custom_code
generated_from_trainer
safetensors
token-classification
transformers

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Contributors

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