Instructions to use Ritwik1998/masked-lm-tpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ritwik1998/masked-lm-tpu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Ritwik1998/masked-lm-tpu")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Ritwik1998/masked-lm-tpu") model = AutoModelForMaskedLM.from_pretrained("Ritwik1998/masked-lm-tpu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Ritwik1998/masked-lm-tpu
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 9.9915
- Train Accuracy: 0.0002
- Validation Loss: 9.9088
- Validation Accuracy: 0.0015
- Epoch: 8
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': 0.0001, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 22325, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 1175, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|---|---|---|---|---|
| 10.2835 | 0.0000 | 10.2748 | 0.0 | 0 |
| 10.2791 | 0.0000 | 10.2665 | 0.0000 | 1 |
| 10.2586 | 0.0 | 10.2434 | 0.0000 | 2 |
| 10.2455 | 0.0000 | 10.2100 | 0.0000 | 3 |
| 10.2063 | 0.0 | 10.1620 | 0.0000 | 4 |
| 10.1658 | 0.0 | 10.1147 | 0.0 | 5 |
| 10.1174 | 0.0 | 10.0558 | 0.0 | 6 |
| 10.0613 | 0.0000 | 9.9800 | 0.0002 | 7 |
| 9.9915 | 0.0002 | 9.9088 | 0.0015 | 8 |
Framework versions
- Transformers 4.35.2
- TensorFlow 2.12.0
- Tokenizers 0.15.0
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Model tree for Ritwik1998/masked-lm-tpu
Base model
FacebookAI/roberta-base