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
Download handler.py from konsman/setfit-messages-generated-v2: direct link, hf CLI and curl.
- Browser
- Download file 850 Bytes
-
https://huggingface.co/konsman/setfit-messages-generated-v2/resolve/main/handler.py
- Command line
-
hf download hf://konsman/setfit-messages-generated-v2/handler.py
-
curl -L -o handler.py https://huggingface.co/konsman/setfit-messages-generated-v2/resolve/main/handler.py
850 Bytes
| 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)] |