metadata
tags:
- generated_from_trainer
model-index:
- name: ibert-roberta-base-finetuned-WikiNeural
results: []
datasets:
- Babelscape/wikineural
language:
- en
metrics:
- accuracy
- f1
- recall
- precision
- seqeval
pipeline_tag: token-classification
ibert-roberta-base-finetuned-WikiNeural
This model is a fine-tuned version of kssteven/ibert-roberta-base.
It achieves the following results on the evaluation set:
- Loss: 0.0878
- Loc: {'precision': 0.9249338624338624, 'recall': 0.9393786733837112, 'f1': 0.9321003082562693, 'number': 5955}
- Misc: {'precision': 0.8304751697034656, 'recall': 0.9185931634064414, 'f1': 0.8723144760296463, 'number': 5061}
- Org: {'precision': 0.9283453237410072, 'recall': 0.9353435778486517, 'f1': 0.9318313113807049, 'number': 3449}
- Per: {'precision': 0.9698098412076064, 'recall': 0.9495201535508637, 'f1': 0.9595577538551062, 'number': 5210}
- Overall Precision: 0.9107
- Overall Recall: 0.9360
- Overall F1: 0.9232
- Overall Accuracy: 0.9909
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-%20I-BERT%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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1092 | 1.0 | 5795 | 0.0987 | {'precision': 0.9124507227332457, 'recall': 0.9328295549958019, 'f1': 0.9225276093996512, 'number': 5955} | {'precision': 0.8003130979300748, 'recall': 0.9091088717644734, 'f1': 0.8512488436632748, 'number': 5061} | {'precision': 0.9142857142857143, 'recall': 0.9278051609162076, 'f1': 0.9209958267376601, 'number': 3449} | {'precision': 0.9714229013693193, 'recall': 0.9395393474088292, 'f1': 0.9552151429407748, 'number': 5210} | 0.8957 | 0.9276 | 0.9114 | 0.9890 |
| 0.0723 | 2.0 | 11590 | 0.0878 | {'precision': 0.9249338624338624, 'recall': 0.9393786733837112, 'f1': 0.9321003082562693, 'number': 5955} | {'precision': 0.8304751697034656, 'recall': 0.9185931634064414, 'f1': 0.8723144760296463, 'number': 5061} | {'precision': 0.9283453237410072, 'recall': 0.9353435778486517, 'f1': 0.9318313113807049, 'number': 3449} | {'precision': 0.9698098412076064, 'recall': 0.9495201535508637, 'f1': 0.9595577538551062, 'number': 5210} | 0.9107 | 0.9360 | 0.9232 | 0.9909 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1
- Datasets 2.13.0
- Tokenizers 0.13.3