Instructions to use ChinweAnthony/model_food_no with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChinweAnthony/model_food_no with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ChinweAnthony/model_food_no")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ChinweAnthony/model_food_no") model = AutoModelForSequenceClassification.from_pretrained("ChinweAnthony/model_food_no") - Notebooks
- Google Colab
- Kaggle
model_food_no
This model is a fine-tuned version of mrm8488/bert-tiny-finetuned-sms-spam-detection on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0905
- Accuracy: 0.98
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:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7849 | 1.0 | 7 | 0.3588 | 0.88 |
| 0.3607 | 2.0 | 14 | 0.2537 | 0.94 |
| 0.2586 | 3.0 | 21 | 0.1939 | 0.96 |
| 0.1837 | 4.0 | 28 | 0.1616 | 0.94 |
| 0.1548 | 5.0 | 35 | 0.1471 | 0.94 |
| 0.1390 | 6.0 | 42 | 0.1145 | 0.98 |
| 0.1128 | 7.0 | 49 | 0.1009 | 0.96 |
| 0.0982 | 8.0 | 56 | 0.0939 | 0.98 |
| 0.0898 | 9.0 | 63 | 0.0920 | 0.98 |
| 0.0920 | 10.0 | 70 | 0.0905 | 0.98 |
Framework versions
- Transformers 5.10.2
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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