Instructions to use chosenone80/ner-test-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chosenone80/ner-test-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="chosenone80/ner-test-1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("chosenone80/ner-test-1") model = AutoModelForTokenClassification.from_pretrained("chosenone80/ner-test-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: CAMeL-Lab/bert-base-arabic-camelbert-ca | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: chosenone80/ner-test-1 | |
| 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. --> | |
| # chosenone80/ner-test-1 | |
| This model is a fine-tuned version of [CAMeL-Lab/bert-base-arabic-camelbert-ca](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 0.2042 | |
| - Validation Loss: 0.1926 | |
| - Train Precision: 0.8860 | |
| - Train Recall: 0.8988 | |
| - Train F1: 0.8923 | |
| - Train Accuracy: 0.9364 | |
| - 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': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16141, '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 | | |
| |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | |
| | 0.2042 | 0.1926 | 0.8860 | 0.8988 | 0.8923 | 0.9364 | 0 | | |
| ### Framework versions | |
| - Transformers 4.35.2 | |
| - TensorFlow 2.15.0 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.0 | |