Instructions to use boronbrown48/1_topic_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boronbrown48/1_topic_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="boronbrown48/1_topic_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("boronbrown48/1_topic_classification") model = AutoModelForSequenceClassification.from_pretrained("boronbrown48/1_topic_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval.txt from boronbrown48/1_topic_classification: direct link, hf CLI and curl.
- Browser
- Download file 298 Bytes
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https://huggingface.co/boronbrown48/1_topic_classification/resolve/main/eval.txt
- Command line
-
hf download hf://boronbrown48/1_topic_classification/eval.txt
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curl -L -o eval.txt https://huggingface.co/boronbrown48/1_topic_classification/resolve/main/eval.txt
298 Bytes
| {'epoch': 4.13, | |
| 'eval_accuracy': 0.9283018867924528, | |
| 'eval_f1-macro': 0.7835303930477823, | |
| 'eval_loss': 0.31459057331085205, | |
| 'eval_precision': 0.7974073129596689, | |
| 'eval_recall': 0.7740961320523125, | |
| 'eval_runtime': 25.4833, | |
| 'eval_samples_per_second': 239.177, | |
| 'eval_steps_per_second': 29.902} |