Instructions to use zeroshot/sst2-distilbert-sparse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zeroshot/sst2-distilbert-sparse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zeroshot/sst2-distilbert-sparse")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zeroshot/sst2-distilbert-sparse") model = AutoModelForSequenceClassification.from_pretrained("zeroshot/sst2-distilbert-sparse", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| 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, scheduler="sync") | |
| 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 | |
| } |