Instructions to use San-Analytics/ner-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use San-Analytics/ner-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="San-Analytics/ner-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("San-Analytics/ner-bert") model = AutoModelForTokenClassification.from_pretrained("San-Analytics/ner-bert") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: bert-base-cased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| model-index: | |
| - name: ner-bert | |
| 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. --> | |
| # ner-bert | |
| 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: 0.3014 | |
| - Precision: 0.7740 | |
| - Recall: 0.8080 | |
| - F1: 0.7906 | |
| ## 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: 64 | |
| - 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | |
| | 0.3080 | 1.0 | 2059 | 0.2922 | 0.7640 | 0.8039 | 0.7835 | | |
| | 0.2335 | 2.0 | 4118 | 0.2858 | 0.7755 | 0.8055 | 0.7902 | | |
| | 0.1831 | 3.0 | 6177 | 0.3014 | 0.7740 | 0.8080 | 0.7906 | | |
| ### Framework versions | |
| - Transformers 5.0.0 | |
| - Pytorch 2.10.0+cu128 | |
| - Datasets 4.8.3 | |
| - Tokenizers 0.22.2 | |