Text Classification
Transformers
Safetensors
English
bert
fill-mask
BERT
NeuroBERT
transformer
pre-training
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-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boltuix/NeuroBERT-Mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="boltuix/NeuroBERT-Mini")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("boltuix/NeuroBERT-Mini") model = AutoModelForMaskedLM.from_pretrained("boltuix/NeuroBERT-Mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 611 Bytes
eb25170 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 256,
"initializer_range": 0.02,
"intermediate_size": 1024,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 4,
"num_hidden_layers": 2,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.51.3",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
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