Token Classification
Collection
12 items
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Updated
This model is a fine-tuned version of xlnet-base-cased.
It achieves the following results on the evaluation set:
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
This model is intended to demonstrate my ability to solve a complex problem using technology.
Dataset Source: https://huggingface.co/datasets/Babelscape/wikineural
The following hyperparameters were used during training:
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 |