# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("khadija69/roberta_ASE_clb_large_layers_tail")
model = AutoModelForTokenClassification.from_pretrained("khadija69/roberta_ASE_clb_large_layers_tail", device_map="auto")Quick Links
khadija69/roberta_ASE_clb_large_layers_tail
This model is a fine-tuned version of khadija69/roberta_ASE_kgl_large on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0908
- Validation Loss: 0.2620
- 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: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2400, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 | Epoch |
|---|---|---|
| 0.2264 | 0.2217 | 0 |
| 0.1986 | 0.2090 | 1 |
| 0.1626 | 0.2112 | 2 |
| 0.1300 | 0.2299 | 3 |
| 0.1089 | 0.2454 | 4 |
| 0.0908 | 0.2620 | 5 |
Framework versions
- Transformers 4.41.1
- TensorFlow 2.15.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="khadija69/roberta_ASE_clb_large_layers_tail")