Instructions to use semaj83/ctmatch-clf-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use semaj83/ctmatch-clf-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="semaj83/ctmatch-clf-R")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("semaj83/ctmatch-clf-R") model = AutoModelForSequenceClassification.from_pretrained("semaj83/ctmatch-clf-R", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 67bdba1a5b00c9a3a1772a5bff0873e49350ffe37a4ac0a53d4f71c5de659b4f
- Size of remote file:
- 1.33 GB
- SHA256:
- 8bfe12457279de8120e6a1dad946fde6cf512f381e623db0b6a0bac60694bd39
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.