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

# xlnet-base-cased-finetuned-WikiNeural-PoS

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

It achieves the following results on the evaluation set:
- Loss: 0.0949
- Loc
  - Precision: 0.9289891395154553
  - Recall: 0.9336691855583543
  - F1: 0.931323283082077
  - Number: 5955
- Misc
  - Precision: 0.8191960332920134
  - Recall: 0.9140486069946651
  - F1: 0.8640268957788569
  - Number: 5061
- Org
  - Precision: 0.9199886104783599
  - Recall: 0.9367932734125833
  - F1: 0.9283148972848728
  - Number: 3449
- Per
  - Precision: 0.9687377113645301
  - Recall: 0.9456813819577735
  - F1: 0.9570707070707071
  - Number: 5210
- Overall
  - Precision: 0.9068
  - Recall: 0.9324
  - F1: 0.9194
  - Accuracy: 0.9904

## 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-%20XLNet%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 |
|:-------------:|:-----:|:-----:|:--------------:|:-----------------:|:-------------:|:------------:|:--------------:|:-----------------:|:--------------:|:----------:|:--------:|:--------:|:----------:|:-----------:|:----------:|:----------:|:----------:|:---------:|:----------:|:---------:|:-------:|:----------:|:---------:|
| 0.1119        | 1.0   | 5795  | 0.1067 | 0.9054 | 0.9382 | 0.9215 | 5955 | 0.7967 | 0.8884 | 0.8401 | 5061 | 0.9112 | 0.9226 | 0.9169 | 3449 | 0.9585 | 0.9524 | 0.9554 | 5210 | 0.8899 | 0.9264 | 0.9078 | 0.9887 |
| 0.0724        | 2.0   | 11590 | 0.0949 | 0.9290 | 0.9337 | 0.9313 | 5955 | 0.8192 | 0.9140 | 0.8640 | 5061 | 0.9200 | 0.9368 | 0.9283 | 3449 | 0.9687 | 0.9457 | 0.9571 | 5210 | 0.9068 | 0.9324 | 0.9194 | 0.9904 |
* All values in the above chart are rounded to the nearest ten-thousandths.

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3