Instructions to use Dolmer/my_awesome_google_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dolmer/my_awesome_google_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Dolmer/my_awesome_google_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Dolmer/my_awesome_google_model") model = AutoModelForTokenClassification.from_pretrained("Dolmer/my_awesome_google_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Dolmer/my_awesome_google_model
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.1588
- Validation Loss: 0.2669
- Train Precision: 0.5172
- Train Recall: 0.3230
- Train F1: 0.3976
- Train Accuracy: 0.9400
- Epoch: 1
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': 636, '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 | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|---|---|---|---|---|---|---|
| 0.3366 | 0.3276 | 0.4618 | 0.1376 | 0.2120 | 0.9296 | 0 |
| 0.1588 | 0.2669 | 0.5172 | 0.3230 | 0.3976 | 0.9400 | 1 |
Framework versions
- Transformers 4.48.3
- TensorFlow 2.18.0
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for Dolmer/my_awesome_google_model
Base model
distilbert/distilbert-base-uncased