# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Deepchecks/roberta_base_formality_ranker_onnx")
model = AutoModelForSequenceClassification.from_pretrained("Deepchecks/roberta_base_formality_ranker_onnx")Quick Links
This model represents an ONNX-optimized version of the original roberta-base-formality-ranker model. It has been specifically tailored for GPUs and may exhibit variations in performance when run on CPUs.
Dependencies
Please install the following dependency before you begin working with the model:
pip install optimum[onnxruntime-gpu]
How to use
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSequenceClassification
from optimum.pipelines import pipeline
# load tokenizer and model weights
tokenizer = AutoTokenizer.from_pretrained('Deepchecks/roberta_base_formality_ranker_onnx')
model = ORTModelForSequenceClassification.from_pretrained('Deepchecks/roberta_base_formality_ranker_onnx')
# prepare the pipeline and generate inferences
user_inputs = ["I hope this email finds you well", "I hope this email find you swell", "What's up doc?"]
pip = pipeline(task='text-classification', model=model, tokenizer=tokenizer, device=device, accelerator="ort")
res = pip(user_inputs, batch_size=64, truncation="only_first")
Licensing Information
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Deepchecks/roberta_base_formality_ranker_onnx")