Text Classification
Transformers
Safetensors
bert
research-library
repository-library
metadata-category-classifier
m2
t1_metadata
v2
text-embeddings-inference
Instructions to use PeytonT/metadata-category-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PeytonT/metadata-category-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PeytonT/metadata-category-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PeytonT/metadata-category-classifier") model = AutoModelForSequenceClassification.from_pretrained("PeytonT/metadata-category-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_id": "M2", | |
| "display_name": "Metadata Category Classifier", | |
| "experiment_name": "m2_metadata_category_classifier_draft", | |
| "source_checkpoint": "models/checkpoints/M2", | |
| "repo_id": "PeytonT/metadata-category-classifier", | |
| "version": "v2", | |
| "metrics": { | |
| "eval_loss": 0.003253802889958024, | |
| "eval_accuracy": 1.0, | |
| "eval_macro_f1": 1.0, | |
| "eval_balanced_accuracy": 1.0, | |
| "eval_label_count": 31 | |
| } | |
| } | |