metadata
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.
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
- Overall Recall: 0.9324
- Overall F1: 0.9194
- Overall 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 | Misc | Org | Per | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1119 | 1.0 | 5795 | 0.1067 | {'precision': 0.9053637984119267, 'recall': 0.9382031905961377, 'f1': 0.9214910110506349, 'number': 5955} | {'precision': 0.7967393230551125, 'recall': 0.8883619837976684, 'f1': 0.8400597907324365, 'number': 5061} | {'precision': 0.911225658648339, 'recall': 0.9225862568860539, 'f1': 0.9168707679008787, 'number': 3449} | {'precision': 0.958470156461271, 'recall': 0.9523992322456813, 'f1': 0.9554250505439492, 'number': 5210} | 0.8899 | 0.9264 | 0.9078 | 0.9887 |
| 0.0724 | 2.0 | 11590 | 0.0949 | {'precision': 0.9289891395154553, 'recall': 0.9336691855583543, 'f1': 0.931323283082077, 'number': 5955} | {'precision': 0.8191960332920134, 'recall': 0.9140486069946651, 'f1': 0.8640268957788569, 'number': 5061} | {'precision': 0.9199886104783599, 'recall': 0.9367932734125833, 'f1': 0.9283148972848728, 'number': 3449} | {'precision': 0.9687377113645301, 'recall': 0.9456813819577735, 'f1': 0.9570707070707071, 'number': 5210} | 0.9068 | 0.9324 | 0.9194 | 0.9904 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3