Instructions to use dusersad12/BestCheckpoint-Demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dusersad12/BestCheckpoint-Demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dusersad12/BestCheckpoint-Demo")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dusersad12/BestCheckpoint-Demo") model = AutoModelForSequenceClassification.from_pretrained("dusersad12/BestCheckpoint-Demo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 827 Bytes
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license: apache-2.0
library_name: transformers
---
# BestCheckpoint Demo
This model checkpoint was selected from a hyperparameter sweep as the best-performing run.
## Evaluation Metrics
| Metric | Value |
|---|---|
| Validation Accuracy | 0.861 |
| Validation Loss | 0.412 |
| Validation F1 | 0.855 |
## Training Configuration
The best run used the following hyperparameters:
| Hyperparameter | Value |
|---|---|
| Learning Rate | 3e-05 |
| Weight Decay | 0.1 |
| Epochs | 10 |
| Batch Size | 32 |
## How to Use
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("BestCheckpoint-Demo")
tokenizer = AutoTokenizer.from_pretrained("BestCheckpoint-Demo")
```
## License
This model is released under the Apache 2.0 license.
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