Instructions to use ShynBui/text_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShynBui/text_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ShynBui/text_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ShynBui/text_classification") model = AutoModelForSequenceClassification.from_pretrained("ShynBui/text_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| base_model: vinai/phobert-base-v2 | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: ShynBui/text_classification | |
| 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. --> | |
| # ShynBui/text_classification | |
| This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 0.0079 | |
| - Validation Loss: 0.0370 | |
| - Train Accuracy: 0.9925 | |
| - Epoch: 2 | |
| ## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 14415, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} | |
| - training_precision: float32 | |
| ### Training results | |
| | Train Loss | Validation Loss | Train Accuracy | Epoch | | |
| |:----------:|:---------------:|:--------------:|:-----:| | |
| | 0.0945 | 0.0379 | 0.9892 | 0 | | |
| | 0.0250 | 0.0320 | 0.9911 | 1 | | |
| | 0.0079 | 0.0370 | 0.9925 | 2 | | |
| ### Framework versions | |
| - Transformers 4.35.2 | |
| - TensorFlow 2.14.0 | |
| - Datasets 2.15.0 | |
| - Tokenizers 0.15.0 | |