Instructions to use lmarchyok/e_results-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lmarchyok/e_results-50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lmarchyok/e_results-50")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lmarchyok/e_results-50") model = AutoModelForMaskedLM.from_pretrained("lmarchyok/e_results-50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
lmarchyok/e_results-50
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 2.2942
- Validation Loss: 2.2443
- Epoch: 9
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': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -850, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 1000, '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: mixed_float16
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 3.1356 | 2.7133 | 0 |
| 2.7587 | 2.5315 | 1 |
| 2.6121 | 2.4470 | 2 |
| 2.4944 | 2.3891 | 3 |
| 2.4321 | 2.2817 | 4 |
| 2.3547 | 2.2128 | 5 |
| 2.3097 | 2.2386 | 6 |
| 2.2828 | 2.2630 | 7 |
| 2.2782 | 2.1946 | 8 |
| 2.2942 | 2.2443 | 9 |
Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.13.1
- Tokenizers 0.13.2
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