Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use Erfan2001/Final_PersianTextClassificationModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Erfan2001/Final_PersianTextClassificationModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Erfan2001/Final_PersianTextClassificationModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Erfan2001/Final_PersianTextClassificationModel") model = AutoModelForSequenceClassification.from_pretrained("Erfan2001/Final_PersianTextClassificationModel") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: my-awesome-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. --> | |
| # my-awesome-model | |
| This model was trained from scratch on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| ## 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: None | |
| - training_precision: float32 | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.30.2 | |
| - TensorFlow 2.12.0 | |
| - Tokenizers 0.13.3 | |