Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use alimazhar-110/website_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alimazhar-110/website_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alimazhar-110/website_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("alimazhar-110/website_classification") model = AutoModelForSequenceClassification.from_pretrained("alimazhar-110/website_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dfe890c7e8ed22f231e5708262bdcfac2f444939d4271bfd9e8391ce5a0a5223
- Size of remote file:
- 268 MB
- SHA256:
- 13232586baa1444e65ed4e6bc512239229f570dc059037bc39a92226de5b99f5
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