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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 |
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- Precision: 0.9289891395154553 |
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- Recall: 0.9336691855583543 |
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- F1: 0.931323283082077 |
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- Number: 5955 |
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- Misc |
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- Precision: 0.8191960332920134 |
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- Recall: 0.9140486069946651 |
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- F1: 0.8640268957788569 |
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- Number: 5061 |
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- Org |
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- Precision: 0.9199886104783599 |
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- Recall: 0.9367932734125833 |
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- F1: 0.9283148972848728 |
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- Number: 3449 |
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- Per |
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- Precision: 0.9687377113645301 |
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- Recall: 0.9456813819577735 |
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- F1: 0.9570707070707071 |
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- Number: 5210 |
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- Overall |
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- Precision: 0.9068 |
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- Recall: 0.9324 |
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- F1: 0.9194 |
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- 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 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 | |
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|:-------------:|:-----:|:-----:|:--------------:|:-----------------:|:-------------:|:------------:|:--------------:|:-----------------:|:--------------:|:----------:|:--------:|:--------:|:----------:|:-----------:|:----------:|:----------:|:----------:|:---------:|:----------:|:---------:|:-------:|:----------:|:---------:| |
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| 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 | |
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| 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 | |
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* All values in the above chart are rounded to the nearest ten-thousandths. |
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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 |
