Instructions to use nreimers/BERT-Small-L-4_H-512_A-8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nreimers/BERT-Small-L-4_H-512_A-8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nreimers/BERT-Small-L-4_H-512_A-8")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nreimers/BERT-Small-L-4_H-512_A-8") model = AutoModel.from_pretrained("nreimers/BERT-Small-L-4_H-512_A-8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
BERT-Small-L-4_H-512_A-8
This is a port of the BERT-Small model to Pytorch. It uses 4 layers, a hidden size of 512 and 8 attention heads.
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