Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JohanHeinsen/Labour_ads_demand with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use JohanHeinsen/Labour_ads_demand with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("JohanHeinsen/Labour_ads_demand") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use JohanHeinsen/Labour_ads_demand with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JohanHeinsen/Labour_ads_demand") 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 tokenizer.json from JohanHeinsen/Labour_ads_demand: direct link, hf CLI and curl.
- Browser
- Download file 725 kB
-
https://huggingface.co/JohanHeinsen/Labour_ads_demand/resolve/main/tokenizer.json
- Command line
-
hf download hf://JohanHeinsen/Labour_ads_demand/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/JohanHeinsen/Labour_ads_demand/resolve/main/tokenizer.json
725 kB
File too large to display, you can check the raw version instead.