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---
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 |