Instructions to use philschmid/custom-handler-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/custom-handler-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="philschmid/custom-handler-distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("philschmid/custom-handler-distilbert") model = AutoModelForSequenceClassification.from_pretrained("philschmid/custom-handler-distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| from typing import Dict, List, Any | |
| from transformers import pipeline | |
| class EndpointHandler(): | |
| def __init__(self, path=""): | |
| self.pipeline = pipeline("text-classification",model=path) | |
| def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: | |
| """ | |
| data args: | |
| inputs (:obj: `str`) | |
| date (:obj: `str`) | |
| Return: | |
| A :obj:`list` | `dict`: will be serialized and returned | |
| """ | |
| # get inputs | |
| inputs = data.pop("inputs",data) | |
| date = data.pop("date", None) | |
| # run normal prediction | |
| prediction = self.pipeline(inputs) | |
| return {"inputs": inputs} |