Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use konsman/setfit-messages-generated-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use konsman/setfit-messages-generated-v2 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("konsman/setfit-messages-generated-v2") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use konsman/setfit-messages-generated-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("konsman/setfit-messages-generated-v2") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
File size: 850 Bytes
29a6062 | 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: "low", 1: "medium", 2: "high"}
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)] |