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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bert-base-cased-finetuned-WikiNeural |
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results: [] |
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datasets: |
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- Babelscape/wikineural |
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language: |
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- en |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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- seqeval |
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pipeline_tag: token-classification |
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--- |
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# bert-base-cased-finetuned-WikiNeural |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0881 |
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- Loc |
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- Precision: 0.9282034236330398 |
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- Recall: 0.9378673383711167 |
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- F1: 0.9330103575008353 |
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- Number: 5955 |
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- Misc |
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- Precision: 0.8336608897623727 |
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- Rrecall: 0.9219521833629718 |
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- F1: 0.8755864139613436 |
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- Number: 5061 |
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- Org |
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- Precision: 0.9351851851851852 |
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- Recall: 0.9370832125253696 |
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- F1: 0.9361332367849385 |
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- Number: 3449 |
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- Per |
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- Precision: 0.9728037566034045 |
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- Recall: 0.9543186180422265 |
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- F1: 0.9634725317314214 |
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- Number: 5210 |
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- Overall |
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- Precision: 0.9145 |
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- Recall: 0.9380 |
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- F1: 0.9261 |
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- Accuracy: 0.9912 |
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## Model description |
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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 |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: https://huggingface.co/datasets/Babelscape/wikineural |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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### Training results |
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| 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 | |
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|:-------------:|:-----:|:-----:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:------------:|:-----------:|:------------:|:--------:|:----------:|:--------:|:----------:|:---:| |
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| 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 | |
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| 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 | |
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* All values in the chart above are rounded to the nearest ten-thousandth. |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
