BERT-finetuned-ner-pablo-classifier-then-full-model
This model is a fine-tuned version of pabRomero/BERT-finetuned-ner-pablo-just-classifier on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1088
- Precision: 0.8067
- Recall: 0.8050
- F1: 0.8058
- Accuracy: 0.9711
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1467 | 0.9996 | 652 | 0.1055 | 0.8107 | 0.7464 | 0.7772 | 0.9691 |
| 0.0889 | 1.9992 | 1304 | 0.1018 | 0.8135 | 0.7834 | 0.7982 | 0.9715 |
| 0.0617 | 2.9989 | 1956 | 0.1088 | 0.8067 | 0.8050 | 0.8058 | 0.9711 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for pabRomero/BERT-finetuned-ner-pablo-classifier-then-full-model
Base model
google-bert/bert-base-uncased