Instructions to use helenai/google-bert-bert-base-uncased-ov with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use helenai/google-bert-bert-base-uncased-ov with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="helenai/google-bert-bert-base-uncased-ov")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("helenai/google-bert-bert-base-uncased-ov") model = AutoModelForMaskedLM.from_pretrained("helenai/google-bert-bert-base-uncased-ov") - Notebooks
- Google Colab
- Kaggle
google-bert/bert-base-uncased
This is the google-bert/bert-base-uncased model converted to OpenVINO, for accelerated inference.
An example of how to do inference on this model:
from optimum.intel import OVModelForMaskedLM
from transformers import AutoTokenizer, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/google-bert-bert-base-uncased-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForMaskedLM.from_pretrained(model_id)
pipe = pipeline("fill-mask", model=model, tokenizer=tokenizer)
result = pipe(f"I am a {tokenizer.mask_token} model")
print(result)
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