Instructions to use harishs/distilbert-base-uncased-finetuned-all_data_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harishs/distilbert-base-uncased-finetuned-all_data_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="harishs/distilbert-base-uncased-finetuned-all_data_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("harishs/distilbert-base-uncased-finetuned-all_data_v2") model = AutoModelForMaskedLM.from_pretrained("harishs/distilbert-base-uncased-finetuned-all_data_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
harishs/distilbert-base-uncased-finetuned-all_data_v2
This model is a fine-tuned version of harishs/distilbert-base-uncased-finetuned-all_data_v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.7670
- Validation Loss: 0.8276
- 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': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 601, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 250, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, '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 |
|---|---|---|
| 0.7802 | 0.8278 | 0 |
| 0.7748 | 0.8348 | 1 |
| 0.7670 | 0.8276 | 2 |
Framework versions
- Transformers 4.34.0
- TensorFlow 2.14.0
- Datasets 2.14.5
- Tokenizers 0.14.0
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