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
- Xet hash:
- 78b279b4dbed74aaafae80526c89195fe38d36340e3fcabd0790152d1231bbec
- Size of remote file:
- 440 MB
- SHA256:
- ea309f98a4d491bd2e8d86b8a688099a8eab1ded2c035f5b42b390c4e03f71ce
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.