Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("runningsnake/bert-base-sequence-classification")
model = AutoModelForSequenceClassification.from_pretrained("runningsnake/bert-base-sequence-classification")Quick Links
runningsnake/bert-base-sequence-classification
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0825
- Train Accuracy: 0.9766
- Validation Loss: 0.5064
- Validation Accuracy: 0.8431
- Epoch: 2
Model description
More information needed
Intended uses & limitations
More information needed
How to use
More information needed
Limitations and bias
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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1377, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, '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 |
|---|---|---|---|---|
| 0.2559 | 0.9057 | 0.5082 | 0.8211 | 0 |
| 0.1004 | 0.9673 | 0.5064 | 0.8431 | 1 |
| 0.0825 | 0.9766 | 0.5064 | 0.8431 | 2 |
Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.0
- Tokenizers 0.13.3
Evaluation results
More information needed
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Model tree for runningsnake/bert-base-sequence-classification
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="runningsnake/bert-base-sequence-classification")