Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("RobCaamano/toxicity_distilbert")
model = AutoModelForSequenceClassification.from_pretrained("RobCaamano/toxicity_distilbert")Quick Links
RobCaamano/toxicity_distilbert
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: 0.0875
- Epoch: 5
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: {'inner_optimizer': {'class_name': 'Adam', 'config': {'name': 'Adam', 'learning_rate': 2.9999997e-10, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
Training results
| Train Loss | Epoch |
|---|---|
| 0.0875 | 0 |
Framework versions
- Transformers 4.28.1
- TensorFlow 2.10.0
- Datasets 2.11.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RobCaamano/toxicity_distilbert")