Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use nameissakthi/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nameissakthi/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nameissakthi/my_awesome_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nameissakthi/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("nameissakthi/my_awesome_model") - Notebooks
- Google Colab
- Kaggle
nameissakthi/my_awesome_model
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.2493
- Validation Loss: 0.1894
- Train Accuracy: 0.9244
- 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': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, '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 |
|---|---|---|---|
| 0.2493 | 0.1894 | 0.9244 | 0 |
Framework versions
- Transformers 4.37.0
- TensorFlow 2.10.0
- Datasets 2.16.1
- Tokenizers 0.15.1
- Downloads last month
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Model tree for nameissakthi/my_awesome_model
Base model
distilbert/distilbert-base-uncased