---
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