Instructions to use mrm8488/bert-tiny-finetuned-sms-spam-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/bert-tiny-finetuned-sms-spam-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-sms-spam-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mrm8488/bert-tiny-finetuned-sms-spam-detection") model = AutoModelForSequenceClassification.from_pretrained("mrm8488/bert-tiny-finetuned-sms-spam-detection") - Inference
- Notebooks
- Google Colab
- Kaggle
Meaning of labels?
#2
by redblackbird - opened
It'll return two labels with scores each defining the probability that it is either spam or not spam.
As I understand it, the first value in these spam-detectors is usually the "not spam" probability and the second one the "spam" probability. By testing it with some entries from the training set, I'm pretty sure that this is true for this model as well, so LABEL_0 is "not spam" probabiliyt and LABEL_1 is "spam" probability.
Gotcha! Thank you for your reply!
redblackbird changed discussion status to closed
