Instructions to use Mycsina/bert-crf-ner-conll2003 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mycsina/bert-crf-ner-conll2003 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Mycsina/bert-crf-ner-conll2003", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: bert-base-cased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model-index: | |
| - name: bert-crf-ner-conll2003 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # bert-crf-ner-conll2003 | |
| This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.7776 | |
| - Precision: 0.9454 | |
| - Recall: 0.9495 | |
| - F1: 0.9474 | |
| - Accuracy: 0.9908 | |
| ## 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: 16 | |
| - eval_batch_size: 16 | |
| - 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: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| | 0.7572 | 1.0 | 878 | 0.7882 | 0.9112 | 0.9253 | 0.9182 | 0.9868 | | |
| | 0.2013 | 2.0 | 1756 | 0.7316 | 0.9384 | 0.9456 | 0.9420 | 0.9898 | | |
| | 0.1517 | 3.0 | 2634 | 0.7776 | 0.9454 | 0.9495 | 0.9474 | 0.9908 | | |
| ### Framework versions | |
| - Transformers 5.0.0 | |
| - Pytorch 2.10.0+cu128 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.2 | |