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

# roberta-base-finetuned-WikiNeural

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base).

It achieves the following results on the evaluation set:
- Loss: 0.0871
- Loc
  - Precision: 0.9276567437219359
  - Recall: 0.9366918555835433
  - F1: 0.9321524064171123
  - Number: 5955
- Misc
  - Precision: 0.8334231805929919
  - Recall: 0.916419679905157
  - F1: 0.872953133822699
  - Number: 5061
- Org
  - Precision: 0.9296179258833669
  - Recall: 0.9382429689765149
  - F1: 0.9339105339105339
  - Number: 3449
- Per
  - Precision: 0.9688723570869224
  - Recall: 0.9499040307101727
  - F1: 0.9592944369063772
  - Number: 5210
- Overall
  - Precision: 0.9124
  - Recall: 0.9352
  - F1: 0.9237
  - Accuracy: 0.9910

## 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-%20Roberta-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 |
|:-------------:|:-----:|:-----:|:----------:|:-----------:|:------------:|:------------:|:------------:|:-----------------:|:--------------:|:----------:|:--------:|:----------:|:--------:|:----------:|:--------:|:----------:|:--------:|:----------:|:--------:|:----------:|:--------:|:----------:|:--------:|
| 0.1086   | 1.0   | 5795  | 0.1001  | 0.9149 | 0.9333 | 0.9240 | 5955 | 0.8158 | 0.9030 | 0.8572 | 5061 | 0.9134 | 0.9295 | 0.9214 | 3449 | 0.9642 | 0.9461 | 0.9550 | 5210 | 0.8997 | 0.9282 | 0.9137 | 0.9896 |
| 0.0727   | 2.0   | 11590 | 0.0871  | 0.9277 | 0.9367 | 0.9325 | 5955 | 0.8334 | 0.9164 | 0.8730 | 5061 | 0.9296 | 0.9382 | 0.9339 | 3449 | 0.9689 | 0.9499 | 0.9593 | 5210 | 0.9124 | 0.9352 | 0.9237 | 0.9910 |

* All values in the cahrt above are rounded to the nearest ten-thousandths.

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.1
- Datasets 2.13.0
- Tokenizers 0.13.3