# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("GCopoulos/deberta-base-finetuned-answer_pol")
model = AutoModelForSequenceClassification.from_pretrained("GCopoulos/deberta-base-finetuned-answer_pol")Quick Links
GCopoulos/deberta-base-finetuned-answer_pol
This model is a fine-tuned version of microsoft/deberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0755
- Validation Loss: 0.6949
- Train F1: 0.8489
- 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 7e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 7e-05, 'decay_steps': 653, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 10, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Train F1 | Epoch |
|---|---|---|---|
| 0.5343 | 0.6051 | 0.8389 | 0 |
| 0.1790 | 0.7237 | 0.8194 | 1 |
| 0.0755 | 0.6949 | 0.8489 | 2 |
Framework versions
- Transformers 4.30.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- 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="GCopoulos/deberta-base-finetuned-answer_pol")