Instructions to use hcy60662/banking-using-mybert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hcy60662/banking-using-mybert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hcy60662/banking-using-mybert")# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("hcy60662/banking-using-mybert", dtype="auto") - Notebooks
- Google Colab
- Kaggle
banking-using-mybert
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3440
- F1: 0.9090
- Accuracy: 0.9091
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|---|---|---|---|---|---|
| 1.0905 | 1.0 | 626 | 0.8475 | 0.8018 | 0.8052 |
| 0.5429 | 2.0 | 1252 | 0.5092 | 0.8707 | 0.8708 |
| 0.3192 | 3.0 | 1878 | 0.4176 | 0.8885 | 0.8893 |
| 0.1803 | 4.0 | 2504 | 0.3511 | 0.9072 | 0.9068 |
| 0.0834 | 5.0 | 3130 | 0.3440 | 0.9090 | 0.9091 |
Framework versions
- Transformers 4.48.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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