Instructions to use dusersad12/BestSweepModel-TestRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dusersad12/BestSweepModel-TestRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dusersad12/BestSweepModel-TestRepo")# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("dusersad12/BestSweepModel-TestRepo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from dusersad12/BestSweepModel-TestRepo: direct link, hf CLI and curl.
- Browser
- Download file 1.07 kB
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https://huggingface.co/dusersad12/BestSweepModel-TestRepo/resolve/main/README.md
- Command line
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hf download hf://dusersad12/BestSweepModel-TestRepo/README.md
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curl -L -o README.md https://huggingface.co/dusersad12/BestSweepModel-TestRepo/resolve/main/README.md
1.07 kB
metadata
license: mit
library_name: transformers
sweep-lr5e5-wd001
This model was selected as the best checkpoint from a hyperparameter sweep.
Model Architecture
Training Configuration
| Parameter | Value |
|---|---|
| learning_rate | 5e-05 |
| weight_decay | 0.001 |
| epochs | 30 |
| batch_size | 16 |
| model_arch | deberta-v3-base |
Evaluation Metrics
| Metric | Value |
|---|---|
| val_loss | 0.198 |
| val_accuracy | 0.934 |
| f1_score | 0.921 |
| inference_latency_ms | 14.2 |
Training Loss Curve
Usage
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("BestSweepModel-TestRepo")
tokenizer = AutoTokenizer.from_pretrained("BestSweepModel-TestRepo")
License
This model is released under the MIT License.