Text Classification
Transformers
Safetensors
English
bert
fill-mask
BERT
transformer
nlp
bert-lite
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
on-device-nlp
privacy-first
cpu-inference
speech-intent
offline-nlp
tiny-bert
bert-variant
efficient-nlp
edge-ml
tiny-ml
aiot
embedded-nlp
low-latency
smart-devices
edge-inference
ml-on-microcontrollers
android-nlp
offline-chatbot
esp32-nlp
tflite-compatible
text-embeddings-inference
Instructions to use boltuix/bert-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boltuix/bert-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="boltuix/bert-lite")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("boltuix/bert-lite") model = AutoModelForMaskedLM.from_pretrained("boltuix/bert-lite", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download .gitattributes from boltuix/bert-lite: direct link, hf CLI and curl.
- Browser
- Download file 54 Bytes
-
https://huggingface.co/boltuix/bert-lite/resolve/main/.gitattributes
- Command line
-
hf download hf://boltuix/bert-lite/.gitattributes
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curl -L -o .gitattributes https://huggingface.co/boltuix/bert-lite/resolve/main/.gitattributes
54 Bytes
| model.safetensors filter=lfs diff=lfs merge=lfs -text | |