Instructions to use dvilasuero/instruction-gigo-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dvilasuero/instruction-gigo-detector with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dvilasuero/instruction-gigo-detector") 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] - setfit
How to use dvilasuero/instruction-gigo-detector with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("dvilasuero/instruction-gigo-detector") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download model_head.pkl from dvilasuero/instruction-gigo-detector: direct link, hf CLI and curl.
- Browser
- Download file 16.2 kB
-
https://huggingface.co/dvilasuero/instruction-gigo-detector/resolve/main/model_head.pkl
- Command line
-
hf download hf://dvilasuero/instruction-gigo-detector/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/dvilasuero/instruction-gigo-detector/resolve/main/model_head.pkl
16.2 kB
- Xet hash:
- 4a1c084179fce34002ef6966d60f8733ec4ac8252d51dfc3a70b825ffd0c6e5b
- Size of remote file:
- 16.2 kB
- SHA256:
- df933f01c6dd03cf1549eeb45c709217914c6eb1ee54b637a7188d7b27202ffc
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