Instructions to use optimum/bert-base-NER-neuronx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use optimum/bert-base-NER-neuronx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="optimum/bert-base-NER-neuronx")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("optimum/bert-base-NER-neuronx") model = AutoModelForTokenClassification.from_pretrained("optimum/bert-base-NER-neuronx") - Notebooks
- Google Colab
- Kaggle
INFERENTIA ONLY
from transformers import AutoTokenizer
from optimum.neuron import NeuronBertForTokenClassification
input_shapes = {"batch_size": 1, "sequence_length": 128}
compiler_args = {"auto_cast": "matmul", "auto_cast_type": "bf16"}
neuron_model = NeuronBertForTokenClassification.from_pretrained(
"dslim/bert-base-NER",
export=True,
**input_shapes,
**compiler_args,
)
# Save locally
neuron_model.save_pretrained("bert_base_ner_neuronx")
neuron_model.push_to_hub(
"bert_base_ner_neuronx",
repository_id="optimum/bert-base-NER-neuronx", # Replace with your HF Hub repo id
)
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