Instructions to use israel/testing_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use israel/testing_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="israel/testing_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("israel/testing_model") model = AutoModelForSequenceClassification.from_pretrained("israel/testing_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sentencepiece.bpe.model from israel/testing_model: direct link, hf CLI and curl.
- Browser
- Download file 1.65 MB
-
https://huggingface.co/israel/testing_model/resolve/main/sentencepiece.bpe.model
- Command line
-
hf download hf://israel/testing_model/sentencepiece.bpe.model
-
curl -L -o sentencepiece.bpe.model https://huggingface.co/israel/testing_model/resolve/main/sentencepiece.bpe.model
1.65 MB
- Xet hash:
- a4aaa01457941135d26279bcad6db684e388e3c710aa751b36831048741c35bd
- Size of remote file:
- 1.65 MB
- SHA256:
- 39379df87dca54c26980bc768996371af2537bfe9d33693e2aa90a4c57a25b5c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.