bert-finetuned-ner-synonym-replacement
This model is a fine-tuned version of bert-base-cased on the combined training dataset(tweetner7(train_2021)+augmented dataset(train_2021) using synonym replacment technique. It achieves the following results on the evaluation set:
- Loss: 0.4556
- Precision: 0.6825
- Recall: 0.6704
- F1: 0.6764
- Accuracy: 0.8792
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.5491 | 1.0 | 624 | 0.4161 | 0.7217 | 0.6308 | 0.6732 | 0.8813 |
0.3284 | 2.0 | 1248 | 0.4195 | 0.7077 | 0.6485 | 0.6768 | 0.8820 |
0.2522 | 3.0 | 1872 | 0.4556 | 0.6825 | 0.6704 | 0.6764 | 0.8792 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.12.1
- Datasets 2.10.1
- Tokenizers 0.12.1
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