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---
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](https://huggingface.co/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 |