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
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: apache-2.0
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library_name: transformers
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---
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# BestCheckpoint Demo
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This model checkpoint was selected from a hyperparameter sweep as the best-performing run.
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## Evaluation Metrics
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| Metric | Value |
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|---|---|
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| Validation Accuracy | {RESULT} |
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| Validation Loss | {RESULT} |
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| Validation F1 | {RESULT} |
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## Training Configuration
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The best run used the following hyperparameters:
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| Hyperparameter | Value |
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|---|---|
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| Learning Rate | {RESULT} |
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| Weight Decay | {RESULT} |
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| Epochs | {RESULT} |
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| Batch Size | {RESULT} |
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## How to Use
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```python
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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model = AutoModelForSequenceClassification.from_pretrained("BestCheckpoint-Demo")
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tokenizer = AutoTokenizer.from_pretrained("BestCheckpoint-Demo")
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```
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## License
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This model is released under the Apache 2.0 license.
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