Instructions to use S2312dal/M1_MLM_cross with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use S2312dal/M1_MLM_cross with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="S2312dal/M1_MLM_cross")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("S2312dal/M1_MLM_cross") model = AutoModelForSequenceClassification.from_pretrained("S2312dal/M1_MLM_cross", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from S2312dal/M1_MLM_cross: direct link, hf CLI and curl.
- Browser
- Download file 46.8 MB
-
https://huggingface.co/S2312dal/M1_MLM_cross/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://S2312dal/M1_MLM_cross/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/S2312dal/M1_MLM_cross/resolve/main/pytorch_model.bin
46.8 MB
- Xet hash:
- 55217b45b2588f490f6bed87d5c3077ae66536c4df5f4a6feba62c4d8beff00d
- Size of remote file:
- 46.8 MB
- SHA256:
- f37faaea797d87287140583e74f091daf924c82a659dc7dfdec40c9b0c5c0be0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.