Text Classification
Transformers
TensorBoard
Safetensors
English
modernbert
Generated from Trainer
topic-detection
web-content-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use davanstrien/ModernBERT-web-topics-1m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davanstrien/ModernBERT-web-topics-1m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="davanstrien/ModernBERT-web-topics-1m")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("davanstrien/ModernBERT-web-topics-1m") model = AutoModelForSequenceClassification.from_pretrained("davanstrien/ModernBERT-web-topics-1m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from davanstrien/ModernBERT-web-topics-1m: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/davanstrien/ModernBERT-web-topics-1m/resolve/main/training_args.bin
- Command line
-
hf download hf://davanstrien/ModernBERT-web-topics-1m/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/davanstrien/ModernBERT-web-topics-1m/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- a2d29ee1473b1752107b43e67d9c0508408194fbd3d5e3665186247da1c6282e
- Size of remote file:
- 5.37 kB
- SHA256:
- 37e226e934e9fb34ee696b9d8c903b851a515d3ce4f6896a82cc6fc8e1d6dc8e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.