Instructions to use YarBar/bert-finetuned-ner-17-2epochs_even_more_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YarBar/bert-finetuned-ner-17-2epochs_even_more_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="YarBar/bert-finetuned-ner-17-2epochs_even_more_data")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("YarBar/bert-finetuned-ner-17-2epochs_even_more_data") model = AutoModelForTokenClassification.from_pretrained("YarBar/bert-finetuned-ner-17-2epochs_even_more_data") - Notebooks
- Google Colab
- Kaggle
bert-finetuned-ner-17-2epochs_even_more_data
This model is a fine-tuned version of FacebookAI/roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2271
- Precision: 0.8671
- Recall: 0.8860
- F1: 0.8764
- Accuracy: 0.9717
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: 32
- eval_batch_size: 128
- 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1861 | 0.1013 | 400 | 0.1766 | 0.4918 | 0.3811 | 0.4294 | 0.9014 |
| 0.1197 | 0.2025 | 800 | 0.1510 | 0.6989 | 0.6392 | 0.6677 | 0.9399 |
| 0.0630 | 0.3038 | 1200 | 0.1601 | 0.7535 | 0.8117 | 0.7815 | 0.9534 |
| 0.0395 | 0.4051 | 1600 | 0.1506 | 0.8215 | 0.8770 | 0.8483 | 0.9652 |
| 0.0288 | 0.5063 | 2000 | 0.1764 | 0.8406 | 0.8582 | 0.8493 | 0.9668 |
| 0.0210 | 0.6076 | 2400 | 0.1632 | 0.8554 | 0.8785 | 0.8668 | 0.9702 |
| 0.0177 | 0.7089 | 2800 | 0.1504 | 0.8677 | 0.8755 | 0.8715 | 0.9717 |
| 0.0133 | 0.8101 | 3200 | 0.1946 | 0.8719 | 0.8785 | 0.8752 | 0.9730 |
| 0.0132 | 0.9114 | 3600 | 0.1760 | 0.8484 | 0.8777 | 0.8628 | 0.9703 |
| 0.0112 | 1.0127 | 4000 | 0.2094 | 0.8468 | 0.8792 | 0.8627 | 0.9687 |
| 0.0087 | 1.1139 | 4400 | 0.2003 | 0.8700 | 0.8837 | 0.8768 | 0.9730 |
| 0.0085 | 1.2152 | 4800 | 0.1958 | 0.8688 | 0.8942 | 0.8813 | 0.9730 |
| 0.0070 | 1.3165 | 5200 | 0.2149 | 0.8495 | 0.8852 | 0.8670 | 0.9706 |
| 0.0064 | 1.4177 | 5600 | 0.2139 | 0.8743 | 0.8822 | 0.8783 | 0.9712 |
| 0.0055 | 1.5190 | 6000 | 0.2070 | 0.8662 | 0.8890 | 0.8775 | 0.9726 |
| 0.0053 | 1.6203 | 6400 | 0.2201 | 0.8603 | 0.8822 | 0.8711 | 0.9710 |
| 0.0045 | 1.7215 | 6800 | 0.2325 | 0.8611 | 0.8882 | 0.8744 | 0.9717 |
| 0.0052 | 1.8228 | 7200 | 0.2173 | 0.8691 | 0.8867 | 0.8778 | 0.9728 |
| 0.0043 | 1.9241 | 7600 | 0.2287 | 0.8662 | 0.8837 | 0.8749 | 0.9715 |
| 0.0043 | 2.0 | 7900 | 0.2271 | 0.8671 | 0.8860 | 0.8764 | 0.9717 |
Framework versions
- Transformers 5.7.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2
- Downloads last month
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Model tree for YarBar/bert-finetuned-ner-17-2epochs_even_more_data
Base model
FacebookAI/roberta-base