Instructions to use boronbrown48/1_model_topic_classification_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boronbrown48/1_model_topic_classification_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="boronbrown48/1_model_topic_classification_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("boronbrown48/1_model_topic_classification_v2") model = AutoModelForSequenceClassification.from_pretrained("boronbrown48/1_model_topic_classification_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval.txt from boronbrown48/1_model_topic_classification_v2: direct link, hf CLI and curl.
- Browser
- Download file 294 Bytes
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https://huggingface.co/boronbrown48/1_model_topic_classification_v2/resolve/main/eval.txt
- Command line
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hf download hf://boronbrown48/1_model_topic_classification_v2/eval.txt
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curl -L -o eval.txt https://huggingface.co/boronbrown48/1_model_topic_classification_v2/resolve/main/eval.txt
294 Bytes
| {'epoch': 3.54, | |
| 'eval_accuracy': 0.9243642329778508, | |
| 'eval_f1-macro': 0.7717268068768623, | |
| 'eval_loss': 0.3193516135215759, | |
| 'eval_precision': 0.7777396877295429, | |
| 'eval_recall': 0.7715675339890613, | |
| 'eval_runtime': 76.7377, | |
| 'eval_samples_per_second': 79.426, | |
| 'eval_steps_per_second': 9.93} |