Text Classification
Transformers
ONNX
Safetensors
modernbert
ner
on-device
privacy
flowx
openner
logistics
de-identification
text-embeddings-inference
Instructions to use flowxai/hscodeclassify with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/hscodeclassify with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="flowxai/hscodeclassify")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/hscodeclassify") model = AutoModelForSequenceClassification.from_pretrained("flowxai/hscodeclassify", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0cadc61d18990ea01bcf2c2641390bfa272b81ada85735c2597fc7491ae93145
- Size of remote file:
- 598 MB
- SHA256:
- e85fadff2c128f420cd69db59d97e1ecdad8123b17095114276637cac3f5cc60
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