Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use PracticalWork/ModernBERT-large-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PracticalWork/ModernBERT-large-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PracticalWork/ModernBERT-large-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PracticalWork/ModernBERT-large-classifier") model = AutoModelForSequenceClassification.from_pretrained("PracticalWork/ModernBERT-large-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from PracticalWork/ModernBERT-large-classifier: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/PracticalWork/ModernBERT-large-classifier/resolve/main/training_args.bin
- Command line
-
hf download hf://PracticalWork/ModernBERT-large-classifier/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/PracticalWork/ModernBERT-large-classifier/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- ad7d729ab52d51cf82a5c6569fd02f611db1045fdc03fec9f530166983ee2f32
- Size of remote file:
- 5.37 kB
- SHA256:
- 6227b777d4616c1ea78f6594ef175ad886da55672960bcd346748652010337d4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.