Text Classification
Transformers
TensorFlow
roberta
generated_from_keras_callback
text-embeddings-inference
Instructions to use MarioPenguin/roberta-model-english with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MarioPenguin/roberta-model-english with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MarioPenguin/roberta-model-english")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MarioPenguin/roberta-model-english") model = AutoModelForSequenceClassification.from_pretrained("MarioPenguin/roberta-model-english") - Notebooks
- Google Colab
- Kaggle
roberta-model-english
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: 0.1140
- Train Accuracy: 0.9596
- Validation Loss: 0.2166
- Validation Accuracy: 0.9301
- 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': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|---|---|---|---|---|
| 0.2922 | 0.8804 | 0.2054 | 0.9162 | 0 |
| 0.1710 | 0.9352 | 0.1879 | 0.9353 | 1 |
| 0.1140 | 0.9596 | 0.2166 | 0.9301 | 2 |
Framework versions
- Transformers 4.16.2
- TensorFlow 2.7.0
- Tokenizers 0.11.0
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