Instructions to use faridlazuarda/data_laundry with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use faridlazuarda/data_laundry with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="faridlazuarda/data_laundry")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("faridlazuarda/data_laundry") model = AutoModelForSequenceClassification.from_pretrained("faridlazuarda/data_laundry", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from faridlazuarda/data_laundry: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/faridlazuarda/data_laundry/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://faridlazuarda/data_laundry/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/faridlazuarda/data_laundry/resolve/main/pytorch_model.bin
17.1 MB
- Xet hash:
- f956629c59b80276d8f04a224ecb4814071fe5e19854c93a5407414adb5780c8
- Size of remote file:
- 17.1 MB
- SHA256:
- 8b0ff923702f44c15ee4016fb795db3ceecc18dd440520eba3ee0c2a4ee76b21
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.