Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("prahlad/rotten_model")
model = AutoModelForSequenceClassification.from_pretrained("prahlad/rotten_model")Quick Links
prahlad/rotten_model
This model is a fine-tuned version of bert-base-uncased on rotten_tomatoes movie review dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.4876
- Train Accuracy: 0.7620
- Validation Loss: 0.5001
- Validation Accuracy: 0.7842
- 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': 5e-05, 'decay_steps': 12795, '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-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|---|---|---|---|---|
| 0.4876 | 0.7620 | 0.5001 | 0.7842 | 0 |
Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prahlad/rotten_model")