Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use Sifter/product-classify-name-uom-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sifter/product-classify-name-uom-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sifter/product-classify-name-uom-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sifter/product-classify-name-uom-v2") model = AutoModelForSequenceClassification.from_pretrained("Sifter/product-classify-name-uom-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f78591d18f8d7750ab7875bbab696a88f1b93093a1bb7f9d9a5dabea8a5a8fe
- Size of remote file:
- 5.37 kB
- SHA256:
- bcd4878ac2603c3f54824787874a91dcb0497a6534c4f041458f7782fdd758bf
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