Text Classification
Transformers
Safetensors
English
bert
fill-mask
BERT
NeuroBERT
transformer
nlp
tiny-bert
edge-ai
low-resource
micro-nlp
quantized
iot
wearable-ai
offline-assistant
intent-detection
real-time
smart-home
embedded-systems
command-classification
toy-robotics
voice-ai
eco-ai
english
lightweight
mobile-nlp
ner
text-embeddings-inference
Instructions to use boltuix/NeuroBERT-Tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boltuix/NeuroBERT-Tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="boltuix/NeuroBERT-Tiny")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("boltuix/NeuroBERT-Tiny") model = AutoModelForMaskedLM.from_pretrained("boltuix/NeuroBERT-Tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from boltuix/NeuroBERT-Tiny: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/boltuix/NeuroBERT-Tiny/resolve/main/tokenizer.json
- Command line
-
hf download hf://boltuix/NeuroBERT-Tiny/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/boltuix/NeuroBERT-Tiny/resolve/main/tokenizer.json
711 kB
File too large to display, you can check the raw version instead.