Instructions to use helenai/dslim-bert-base-NER-ov-fp32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use helenai/dslim-bert-base-NER-ov-fp32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="helenai/dslim-bert-base-NER-ov-fp32")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("helenai/dslim-bert-base-NER-ov-fp32") model = AutoModelForTokenClassification.from_pretrained("helenai/dslim-bert-base-NER-ov-fp32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
dslim/bert-base-NER
This is the dslim/bert-base-NER model converted to OpenVINO, for accellerated inference.
An example of how to do inference on this model:
from optimum.intel.openvino import OVModelForTokenClassification
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/dslim-bert-base-NER-ov-fp32"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForTokenClassification.from_pretrained(model_id)
pipe = pipeline("token-classification", model=model, tokenizer=tokenizer)
result = pipe("My name is Wolfgang and I live in Berlin")
print(result)
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