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videberta-large-ner-ghtk-hirach_NER-first_1000_data-3090-15Nov

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metadata
library_name: transformers
base_model: Fsoft-AIC/videberta-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: videberta-large-ner-ghtk-hirach_NER-first_1000_data-3090-15Nov
    results: []

videberta-large-ner-ghtk-hirach_NER-first_1000_data-3090-15Nov

This model is a fine-tuned version of Fsoft-AIC/videberta-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0976
  • Accuracy: 0.9816
  • F1: 0.0
  • Precision: 0.0
  • Recall: 0.0

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: 2.5e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 40

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
No log 1.0 250 0.0903 0.9825 0.0 0.0 0.0
0.1532 2.0 500 0.0942 0.9825 0.0 0.0 0.0
0.1532 3.0 750 0.0947 0.9825 0.0 0.0 0.0
0.0772 4.0 1000 0.0956 0.9825 0.0 0.0 0.0
0.0772 5.0 1250 0.0964 0.9825 0.0 0.0 0.0
0.0769 6.0 1500 0.0952 0.9825 0.0 0.0 0.0
0.0769 7.0 1750 0.0988 0.9825 0.0 0.0 0.0
0.0766 8.0 2000 0.0983 0.9825 0.0 0.0 0.0
0.0766 9.0 2250 0.0971 0.9825 0.0 0.0 0.0
0.0769 10.0 2500 0.0984 0.9825 0.0 0.0 0.0
0.0769 11.0 2750 0.1002 0.9825 0.0 0.0 0.0
0.0768 12.0 3000 0.0989 0.9825 0.0 0.0 0.0
0.0768 13.0 3250 0.0994 0.9825 0.0 0.0 0.0
0.0766 14.0 3500 0.0994 0.9825 0.0 0.0 0.0
0.0766 15.0 3750 0.0994 0.9825 0.0 0.0 0.0
0.0763 16.0 4000 0.0991 0.9825 0.0 0.0 0.0
0.0763 17.0 4250 0.1011 0.9825 0.0 0.0 0.0
0.0766 18.0 4500 0.0995 0.9825 0.0 0.0 0.0
0.0766 19.0 4750 0.1003 0.9825 0.0 0.0 0.0
0.0761 20.0 5000 0.0996 0.9825 0.0 0.0 0.0
0.0761 21.0 5250 0.1004 0.9825 0.0 0.0 0.0
0.0757 22.0 5500 0.1002 0.9825 0.0 0.0 0.0
0.0757 23.0 5750 0.0993 0.9825 0.0 0.0 0.0
0.0749 24.0 6000 0.0981 0.9825 0.0 0.0 0.0
0.0749 25.0 6250 0.0986 0.9825 0.0 0.0 0.0
0.0739 26.0 6500 0.0991 0.9825 0.0 0.0 0.0
0.0739 27.0 6750 0.0983 0.9825 0.0 0.0 0.0
0.0723 28.0 7000 0.0985 0.9808 0.0 0.0 0.0
0.0723 29.0 7250 0.1009 0.9800 0.0 0.0 0.0
0.0713 30.0 7500 0.0995 0.9812 0.0 0.0 0.0
0.0713 31.0 7750 0.0983 0.9816 0.0 0.0 0.0
0.0699 32.0 8000 0.0969 0.9820 0.0 0.0 0.0
0.0699 33.0 8250 0.0982 0.9816 0.0 0.0 0.0
0.0691 34.0 8500 0.0973 0.9816 0.0 0.0 0.0
0.0691 35.0 8750 0.0984 0.9812 0.0 0.0 0.0
0.0684 36.0 9000 0.0977 0.9816 0.0 0.0 0.0
0.0684 37.0 9250 0.0978 0.9816 0.0 0.0 0.0
0.0676 38.0 9500 0.0972 0.9820 0.0 0.0 0.0
0.0676 39.0 9750 0.0977 0.9816 0.0 0.0 0.0
0.0671 40.0 10000 0.0976 0.9816 0.0 0.0 0.0

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.1.0
  • Tokenizers 0.19.1