Instructions to use zeroshot/sst2-obert-sparse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zeroshot/sst2-obert-sparse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zeroshot/sst2-obert-sparse")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zeroshot/sst2-obert-sparse") model = AutoModelForSequenceClassification.from_pretrained("zeroshot/sst2-obert-sparse", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 711 Bytes
7e0b8ee 6c63fe2 7e0b8ee 6c63fe2 7e0b8ee 6c63fe2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | from typing import Dict, Any
from deepsparse import Pipeline
from time import perf_counter
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
} |