Instructions to use dusersad12/BestCheckpoint-ExpRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dusersad12/BestCheckpoint-ExpRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dusersad12/BestCheckpoint-ExpRepo")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dusersad12/BestCheckpoint-ExpRepo") model = AutoModelForCausalLM.from_pretrained("dusersad12/BestCheckpoint-ExpRepo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,005 Bytes
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license: apache-2.0
library_name: transformers
tags:
- text-classification
- experiment
---
# BestCheckpoint Model
This model was selected as the best checkpoint from a series of hyperparameter search experiments. It achieved the highest validation F1 score across all runs.
## Training Details
| Hyperparameter | Value |
|---|---|
| Best Run | run_swift_hawk |
| Validation F1 | 0.891 |
| Validation Loss | 0.287 |
| Epoch | 7 |
| Learning Rate | 2e-05 |
## Evaluation
The model was evaluated on a held-out validation set. The primary selection criterion was `val_f1`.
## Usage
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("BestCheckpoint-ExpRepo")
tokenizer = AutoTokenizer.from_pretrained("BestCheckpoint-ExpRepo")
```
## Limitations
This model was trained for research purposes and may not generalize well to all domains.
## License
This model is released under the Apache 2.0 license.
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