Instructions to use claritylab/MARS-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use claritylab/MARS-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="claritylab/MARS-Encoder")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("claritylab/MARS-Encoder") model = AutoModelForSequenceClassification.from_pretrained("claritylab/MARS-Encoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 76d30a44e667de9f92cd2570c981fe9605fffc2270a1f4a7df7b4426ee974c2d
- Size of remote file:
- 499 MB
- SHA256:
- 505f2b7f37e557aea3a7ae652ad519ebefd9712473143f234ca8cbd8febb3a94
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.