Text Classification
Transformers
Safetensors
English
modernbert
llm-routing
capability-vector
system-one
text-embeddings-inference
Instructions to use regolo/brick-sysone-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use regolo/brick-sysone-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="regolo/brick-sysone-V1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("regolo/brick-sysone-V1") model = AutoModelForSequenceClassification.from_pretrained("regolo/brick-sysone-V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from regolo/brick-sysone-V1: direct link, hf CLI and curl.
- Browser
- Download file 5.27 kB
-
https://huggingface.co/regolo/brick-sysone-V1/resolve/main/training_args.bin
- Command line
-
hf download hf://regolo/brick-sysone-V1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/regolo/brick-sysone-V1/resolve/main/training_args.bin
5.27 kB
- Xet hash:
- f32d8e04a2772f0bb76d4fe7929f664615a7f0152028667b28c73f88157a015a
- Size of remote file:
- 5.27 kB
- SHA256:
- 4654b74dcac53fc17803bd6c488c4b5350d8f10e60abedb3ad975dbeb1b073d2
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