Instructions to use nihalbaig/setfit_bn_hatespeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nihalbaig/setfit_bn_hatespeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nihalbaig/setfit_bn_hatespeech")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nihalbaig/setfit_bn_hatespeech") model = AutoModel.from_pretrained("nihalbaig/setfit_bn_hatespeech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 848 Bytes
31e2f16 | 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, List, Any
from setfit import SetFitModel
class EndpointHandler:
def __init__(self, path=""):
# load model
self.model = SetFitModel.from_pretrained(path)
# ag_news id to label mapping
self.id2label = {0: "No Hate", 1: "Hate Speech"}
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str`)
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
# get inputs
inputs = data.pop("inputs", data)
if isinstance(inputs, str):
inputs = [inputs]
# run normal prediction
scores = self.model.predict_proba(inputs)[0]
return [{"label": self.id2label[i], "score": score.item()} for i, score in enumerate(scores)] |