metadata
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-finetuned-WikiNeural
results: []
datasets:
- Babelscape/wikineural
language:
- en
metrics:
- accuracy
- f1
- recall
- precision
- seqeval
pipeline_tag: token-classification
bert-base-cased-finetuned-WikiNeural
This model is a fine-tuned version of bert-base-cased. It achieves the following results on the evaluation set:
- Loss: 0.0881
- Loc: {'precision': 0.9282034236330398, 'recall': 0.9378673383711167, 'f1': 0.9330103575008353, 'number': 5955}
- Misc: {'precision': 0.8336608897623727, 'recall': 0.9219521833629718, 'f1': 0.8755864139613436, 'number': 5061}
- Org: {'precision': 0.9351851851851852, 'recall': 0.9370832125253696, 'f1': 0.9361332367849385, 'number': 3449}
- Per: {'precision': 0.9728037566034045, 'recall': 0.9543186180422265, 'f1': 0.9634725317314214, 'number': 5210}
- Overall Precision: 0.9145
- Overall Recall: 0.9380
- Overall F1: 0.9261
- Overall Accuracy: 0.9912
Model description
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-%20BERT-Base%20Transformer.ipynb
Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
Training and evaluation data
Dataset Source: https://huggingface.co/datasets/Babelscape/wikineural
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Loc | Misc | Org | Per | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1 | 1.0 | 5795 | 0.0943 | {'precision': 0.9075480846937126, 'recall': 0.9429051217464316, 'f1': 0.9248888156811068, 'number': 5955} | {'precision': 0.8320190720704199, 'recall': 0.8964631495751828, 'f1': 0.8630397565151225, 'number': 5061} | {'precision': 0.9151428571428571, 'recall': 0.9286749782545666, 'f1': 0.9218592603252267, 'number': 3449} | {'precision': 0.9683036587751908, 'recall': 0.9499040307101727, 'f1': 0.9590155992636372, 'number': 5210} | 0.9039 | 0.9303 | 0.9169 | 0.9901 |
| 0.0578 | 2.0 | 11590 | 0.0881 | {'precision': 0.9282034236330398, 'recall': 0.9378673383711167, 'f1': 0.9330103575008353, 'number': 5955} | {'precision': 0.8336608897623727, 'recall': 0.9219521833629718, 'f1': 0.8755864139613436, 'number': 5061} | {'precision': 0.9351851851851852, 'recall': 0.9370832125253696, 'f1': 0.9361332367849385, 'number': 3449} | {'precision': 0.9728037566034045, 'recall': 0.9543186180422265, 'f1': 0.9634725317314214, 'number': 5210} | 0.9145 | 0.9380 | 0.9261 | 0.9912 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3