Instructions to use aitechguy/bert-ner-cpp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aitechguy/bert-ner-cpp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="aitechguy/bert-ner-cpp")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("aitechguy/bert-ner-cpp") model = AutoModelForTokenClassification.from_pretrained("aitechguy/bert-ner-cpp") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| datasets: | |
| - eriktks/conll2003 | |
| language: | |
| - en | |
| metrics: | |
| - accuracy | |
| base_model: | |
| - distilbert/distilbert-base-uncased | |
| pipeline_tag: token-classification | |
| tags: | |
| - code | |
| - finance | |
| - legal | |
| - medical | |
| - chemistry | |
| - biology | |
| # aitechguy/distilbert_base_uncased_ner | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 3.5619 | |
| - Validation Loss: 3.5082 | |
| - Epoch: 0 | |
| ## 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: | |
| - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -996, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} | |
| - training_precision: mixed_float16 | |
| ### Training results | |
| | Train Loss | Validation Loss | Epoch | | |
| |:----------:|:---------------:|:-----:| | |
| | 3.5619 | 3.5082 | 0 | | |
| ### Framework versions | |
| - Transformers 4.35.0 | |
| - TensorFlow 2.14.0 | |
| - Datasets 2.14.6 | |
| - Tokenizers 0.14.1 |