Text Classification
Transformers
Safetensors
English
Latin
Greek
distilbert
text-embeddings-inference
Instructions to use sjhuskey/distilbert_multilingual_cased_greek_latin_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sjhuskey/distilbert_multilingual_cased_greek_latin_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sjhuskey/distilbert_multilingual_cased_greek_latin_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sjhuskey/distilbert_multilingual_cased_greek_latin_classifier") model = AutoModelForSequenceClassification.from_pretrained("sjhuskey/distilbert_multilingual_cased_greek_latin_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 15cdd2db38bf44ccb81e1d9b580cb5bf804c68783e2700a0ffb9ffd8c19061a3
- Size of remote file:
- 541 MB
- SHA256:
- ce699f5f54d99bcf6b7f758648bc2d7c248086c79541a3c93411b5e7168e525e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.