Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="NYUAD-ComNets/ethic_name_model")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("NYUAD-ComNets/ethic_name_model")
model = AutoModelForSequenceClassification.from_pretrained("NYUAD-ComNets/ethic_name_model")Quick Links
NYUAD-ComNets/ethic_name_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: 0.2378
- Validation Loss: 0.1741
- Train Accuracy: 0.946
- 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 465, '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 |
|---|---|---|---|
| 0.6145 | 0.4524 | 0.872 | 0 |
| 0.3995 | 0.2989 | 0.918 | 1 |
| 0.2378 | 0.1741 | 0.946 | 2 |
Framework versions
- Transformers 4.38.2
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for NYUAD-ComNets/ethic_name_model
Base model
distilbert/distilbert-base-uncased
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