Instructions to use Arro94/nova-model-benchmark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arro94/nova-model-benchmark with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Arro94/nova-model-benchmark")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Arro94/nova-model-benchmark") model = AutoModelForSequenceClassification.from_pretrained("Arro94/nova-model-benchmark", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Arro94/nova-model-benchmark: direct link, hf CLI and curl.
- Browser
- Download file 364 Bytes
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https://huggingface.co/Arro94/nova-model-benchmark/resolve/main/README.md
- Command line
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hf download hf://Arro94/nova-model-benchmark/README.md
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curl -L -o README.md https://huggingface.co/Arro94/nova-model-benchmark/resolve/main/README.md
364 Bytes
| license: gpl-3.0 | |
| language: | |
| - sv | |
| pipeline_tag: text-classification | |
| Scores (avg. weighted) | |
| - Accuracy: 0.9007633587786259 | |
| - Precision: 0.9008606422369183 | |
| - Recall: 0.9007633587786259 | |
| - F1: 0.9007595035560719 | |
| Hyperparams | |
| - Max Seq Len: 45 | |
| - Batch Size: 16 | |
| - Learning Rate: 2e-5 | |
| - Epochs: 5 | |
| - Warmup Steps: 147 | |
| - Weight Decay: 0.01 | |
| - Save/Eval Strat: epoch |