Instructions to use ilan541/OncUponTim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ilan541/OncUponTim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ilan541/OncUponTim")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ilan541/OncUponTim") model = AutoModelForSequenceClassification.from_pretrained("ilan541/OncUponTim") - Notebooks
- Google Colab
- Kaggle
OncUponTim
This model is a fine-tuned version of ilan541/OncUponTim on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.5891
- Train Accuracy: 0.7106
- Validation Loss: 0.5824
- Validation Accuracy: 0.7115
- Epoch: 0
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': 0.001, '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 |
|---|---|---|---|---|
| 0.5891 | 0.7106 | 0.5824 | 0.7115 | 0 |
Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Tokenizers 0.12.1
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