Token Classification
Collection
12 items
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Updated
This model is a fine-tuned version of bert-base-multilingual-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/Multilingual/Babelscape-WikiNeural-Joined%20Dataset/Babelscape%20WikiNeural%20Joined%20Dataset%20With%20Multilingual%20BERT.ipynb
This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to test and experiment with this model, but it is at your own risk/peril.
Dataset Soruce: https://huggingface.co/datasets/dmargutierrez/Babelscape-wikineural-joined
The following hyperparameters were used during training:
Train Loss | Epoch | Step | Valid Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
---|---|---|---|---|---|---|---|
0.015 | 1.0 | 102700 | 0.0168 | 0.9957 | 0.9961 | 0.9959 | 0.9947 |
Train Loss | Epoch | Valid 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.015 | 1.0 | 0.0168 | 0.9983 | 0.9982 | 0.9983 | 1,932,180 | 0.9809 | 0.9833 | 0.9821 | 122,787 | 0.9869 | 0.9881 | 0.9875 | 59,813 | 0.9386 | 0.9517 | 0.9451 | 47,216 |
Base model
google-bert/bert-base-multilingual-cased