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:
- 9dc77c621c8c8a6910d6653d793680cffd4dd3365f26ba739d26995febfe58ba
- Size of remote file:
- 2.62 MB
- SHA256:
- 0f1342201b0ea1a65082a751ead0c2c18e2bdbd56cd3286399fe7b31ffa0a61c
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