Text Classification
Transformers
TensorFlow
TensorBoard
deberta-v2
generated_from_keras_callback
text-embeddings-inference
Instructions to use svenbl80/deberta-v3-Base-finetuned-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use svenbl80/deberta-v3-Base-finetuned-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="svenbl80/deberta-v3-Base-finetuned-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("svenbl80/deberta-v3-Base-finetuned-mnli") model = AutoModelForSequenceClassification.from_pretrained("svenbl80/deberta-v3-Base-finetuned-mnli") - Notebooks
- Google Colab
- Kaggle
svenbl80/deberta-v3-Base-finetuned-mnli
This model is a fine-tuned version of microsoft/deberta-v3-Base on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.3306
- Validation Loss: 0.2632
- Train Accuracy: 0.9009
- 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': 24543, '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 |
|---|---|---|---|
| 0.3306 | 0.2632 | 0.9009 | 0 |
Framework versions
- Transformers 4.35.2
- TensorFlow 2.9.1
- Datasets 2.15.0
- Tokenizers 0.15.0
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