Instructions to use paragsmhatre/20_news_group_classifier_open_web_text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paragsmhatre/20_news_group_classifier_open_web_text with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="paragsmhatre/20_news_group_classifier_open_web_text")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("paragsmhatre/20_news_group_classifier_open_web_text") model = AutoModelForSequenceClassification.from_pretrained("paragsmhatre/20_news_group_classifier_open_web_text", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from paragsmhatre/20_news_group_classifier_open_web_text: direct link, hf CLI and curl.
- Browser
- Download file 99.9 MB
-
https://huggingface.co/paragsmhatre/20_news_group_classifier_open_web_text/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://paragsmhatre/20_news_group_classifier_open_web_text/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/paragsmhatre/20_news_group_classifier_open_web_text/resolve/main/pytorch_model.bin
99.9 MB
- Xet hash:
- ff891fe009e46067bbe67a7e696336602d6f6dec1442c657ef8fcde6e78362c1
- Size of remote file:
- 99.9 MB
- SHA256:
- e9994872b5ad6ddf57a94215f2207b2aa39bf2f0a4d20a6b72161b6345dbb7c9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.