Instructions to use mahabharahta/dok_ner_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mahabharahta/dok_ner_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mahabharahta/dok_ner_model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mahabharahta/dok_ner_model") model = AutoModelForTokenClassification.from_pretrained("mahabharahta/dok_ner_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: distilbert-base-uncased | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: mahabharahta/dok_ner_model | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should | |
| probably proofread and complete it, then remove this comment. --> | |
| # mahabharahta/dok_ner_model | |
| 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: 0.0002 | |
| - Validation Loss: 0.0000 | |
| - Train Precision: 1.0 | |
| - Train Recall: 1.0 | |
| - Train F1: 1.0 | |
| - Train Accuracy: 1.0 | |
| - Epoch: 1 | |
| ## 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': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 13971, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} | |
| - training_precision: float32 | |
| ### Training results | |
| | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | | |
| |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | |
| | 0.0081 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | | |
| | 0.0002 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | | |
| ### Framework versions | |
| - Transformers 4.44.2 | |
| - TensorFlow 2.17.0 | |
| - Datasets 3.0.1 | |
| - Tokenizers 0.19.1 | |