# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("zeroshot/sst2-distilbert-sparse")
model = AutoModelForSequenceClassification.from_pretrained("zeroshot/sst2-distilbert-sparse")Quick Links
DISTILBERT RUNNING ON DEEPSPARSE GOES BRHMMMMMMMM. πππ
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Inference endpoints, outside of outliers (4ms) is avg. latency on 2 vCPUs:
Handler for access to inference endpoints
class EndpointHandler:
def __init__(self, path=""):
self.pipeline = Pipeline.create(task="text-classification", model_path=path)
def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
"""
Args:
data (:obj:): prediction input text
"""
inputs = data.pop("inputs", data)
start = perf_counter()
prediction = self.pipeline(inputs)
end = perf_counter()
latency = end - start
return {
"labels": prediction.labels,
"scores": prediction.scores,
"latency (secs.)": latency
}
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zeroshot/sst2-distilbert-sparse")