Instructions to use nreimers/TinyBERT_L-4_H-312_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nreimers/TinyBERT_L-4_H-312_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nreimers/TinyBERT_L-4_H-312_v2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nreimers/TinyBERT_L-4_H-312_v2") model = AutoModel.from_pretrained("nreimers/TinyBERT_L-4_H-312_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("nreimers/TinyBERT_L-4_H-312_v2")
model = AutoModel.from_pretrained("nreimers/TinyBERT_L-4_H-312_v2", device_map="auto")Quick Links
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Check out the documentation for more information.
This is the General_TinyBERT_v2(4layer-312dim) ported to Huggingface transformers.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nreimers/TinyBERT_L-4_H-312_v2")