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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: roberta-base-finetuned-WikiNeural |
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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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- precision |
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- recall |
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- seqeval |
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pipeline_tag: token-classification |
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--- |
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# roberta-base-finetuned-WikiNeural |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0871 |
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- Loc |
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- Precision: 0.9276567437219359 |
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- Recall: 0.9366918555835433 |
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- F1: 0.9321524064171123 |
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- Number: 5955 |
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- Misc |
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- Precision: 0.8334231805929919 |
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- Recall: 0.916419679905157 |
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- F1: 0.872953133822699 |
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- Number: 5061 |
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- Org |
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- Precision: 0.9296179258833669 |
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- Recall: 0.9382429689765149 |
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- F1: 0.9339105339105339 |
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- Number: 3449 |
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- Per |
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- Precision: 0.9688723570869224 |
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- Recall: 0.9499040307101727 |
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- F1: 0.9592944369063772 |
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- Number: 5210 |
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- Overall |
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- Precision: 0.9124 |
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- Recall: 0.9352 |
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- F1: 0.9237 |
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- Accuracy: 0.9910 |
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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-%20Roberta-Base%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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| 0.1086 | 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 | |
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| 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 | |
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* All values in the cahrt above 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 2.0.1 |
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- Datasets 2.13.0 |
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- Tokenizers 0.13.3 |
