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bert-base-token-classification-for-atc-en-uwb-atcc

Token Classification
Transformers
PyTorch
Safetensors
English
bert
text
en-atc
Generated from Trainer
bertraffic
Eval Results
Inference Endpoints
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bert-base-token-classification-for-atc-en-uwb-atcc / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: uwb_atcc
    results: []

uwb_atcc

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0098
  • Precision: 0.9760
  • Recall: 0.9741
  • F1: 0.9750
  • Accuracy: 0.9965

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: 5e-05
  • train_batch_size: 64
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • training_steps: 10000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 0.03 500 0.2282 0.6818 0.7001 0.6908 0.9246
0.3487 0.06 1000 0.1214 0.8163 0.8024 0.8093 0.9631
0.3487 0.1 1500 0.0933 0.8496 0.8544 0.8520 0.9722
0.1124 0.13 2000 0.0693 0.8845 0.8739 0.8791 0.9786
0.1124 0.16 2500 0.0540 0.8993 0.8911 0.8952 0.9817
0.0667 0.19 3000 0.0474 0.9058 0.8929 0.8993 0.9857
0.0667 0.23 3500 0.0418 0.9221 0.9245 0.9233 0.9865
0.0492 0.26 4000 0.0294 0.9369 0.9415 0.9392 0.9903
0.0492 0.29 4500 0.0263 0.9512 0.9446 0.9479 0.9911
0.0372 0.32 5000 0.0223 0.9495 0.9497 0.9496 0.9915
0.0372 0.35 5500 0.0212 0.9530 0.9514 0.9522 0.9923
0.0308 0.39 6000 0.0177 0.9585 0.9560 0.9572 0.9933
0.0308 0.42 6500 0.0169 0.9619 0.9613 0.9616 0.9936
0.0261 0.45 7000 0.0140 0.9689 0.9662 0.9676 0.9951
0.0261 0.48 7500 0.0130 0.9652 0.9629 0.9641 0.9945
0.0214 0.51 8000 0.0127 0.9676 0.9635 0.9656 0.9953
0.0214 0.55 8500 0.0109 0.9714 0.9708 0.9711 0.9959
0.0177 0.58 9000 0.0103 0.9740 0.9727 0.9734 0.9961
0.0177 0.61 9500 0.0101 0.9768 0.9744 0.9756 0.9963
0.0159 0.64 10000 0.0098 0.9760 0.9741 0.9750 0.9965

Framework versions

  • Transformers 4.24.0
  • Pytorch 1.13.0+cu117
  • Datasets 2.7.0
  • Tokenizers 0.13.2