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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: xlnet-base-cased-finetuned-WikiCorpus-PoS |
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results: [] |
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datasets: |
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- Babelscape/wikineural |
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language: |
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- en |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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- seqeval |
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pipeline_tag: token-classification |
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--- |
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# xlnet-base-cased-finetuned-WikiNeural-PoS |
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This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0949 |
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- Loc: {'precision': 0.9289891395154553, 'recall': 0.9336691855583543, 'f1': 0.931323283082077, 'number': 5955} |
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- Misc: {'precision': 0.8191960332920134, 'recall': 0.9140486069946651, 'f1': 0.8640268957788569, 'number': 5061} |
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- Org: {'precision': 0.9199886104783599, 'recall': 0.9367932734125833, 'f1': 0.9283148972848728, 'number': 3449} |
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- Per: {'precision': 0.9687377113645301, 'recall': 0.9456813819577735, 'f1': 0.9570707070707071, 'number': 5210} |
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- Overall Precision: 0.9068 |
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- Overall Recall: 0.9324 |
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- Overall F1: 0.9194 |
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- Overall Accuracy: 0.9904 |
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## Model description |
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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 |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: https://huggingface.co/datasets/Babelscape/wikineural |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Loc | Misc | Org | Per | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 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 | |
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| 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 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
