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bert-base-cased-finetuned-WikiNeural

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metadata
license: apache-2.0
tags:
  - generated_from_trainer
model-index:
  - name: bert-base-cased-finetuned-WikiNeural
    results: []
datasets:
  - Babelscape/wikineural
language:
  - en
metrics:
  - accuracy
  - f1
  - recall
  - precision
  - seqeval
pipeline_tag: token-classification

bert-base-cased-finetuned-WikiNeural

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

  • Loss: 0.0881
  • Loc
    • Precision: 0.9282034236330398
    • Recall: 0.9378673383711167
    • F1: 0.9330103575008353
    • Number: 5955
  • Misc
    • Precision: 0.8336608897623727
    • Rrecall: 0.9219521833629718
    • F1: 0.8755864139613436
    • Number: 5061
  • Org
    • Precision: 0.9351851851851852
    • Recall: 0.9370832125253696
    • F1: 0.9361332367849385
    • Number: 3449
  • Per
    • Precision: 0.9728037566034045
    • Recall: 0.9543186180422265
    • F1: 0.9634725317314214
    • Number: 5210
  • Overall
    • Precision: 0.9145
    • Recall: 0.9380
    • F1: 0.9261
    • Accuracy: 0.9912

Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Token%20Classification/Monolingual/WikiNeural%20-%20Transformer%20Comparison/POS%20Project%20with%20Wikineural%20Dataset%20-%20BERT-Base%20Transformer.ipynb

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://huggingface.co/datasets/Babelscape/wikineural

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss Loc Precision Loc Recall Loc F1 Loc Number Misc Precision Misc Recall Misc F1 Misc Number Org Precision Org Recall Org F1 Org Number Per Precision Per Recall Per F1 Per Number Overall Precision Overall Recall Overall F1 Overall Accuracy
0.1 1.0 5795 0.0943 0.9075 0.9429 0.9249 5955 0.8320 0.8965 0.8630 5061 0.9151 0.9287 0.9219 3449 0.9683 0.9499 0.9590 5210 0.9039 0.9303 0.9169 0.9901
0.0578 2.0 11590 0.0881 0.9282 0.9379 0.9330 5955 0.8337 0.9220 0.8756 5061 0.9352 0.9371 0.9361 3449 0.9728 0.9543 0.9635 5210 0.9145 0.9380 0.9261 0.9912
  • All values in the chart above are rounded to the nearest ten-thousandth.

Framework versions

  • Transformers 4.28.1
  • Pytorch 2.0.0
  • Datasets 2.11.0
  • Tokenizers 0.13.3