Instructions to use textattack/distilbert-base-uncased-ag-news with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/distilbert-base-uncased-ag-news with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/distilbert-base-uncased-ag-news")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/distilbert-base-uncased-ag-news") model = AutoModelForSequenceClassification.from_pretrained("textattack/distilbert-base-uncased-ag-news", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from textattack/distilbert-base-uncased-ag-news: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/textattack/distilbert-base-uncased-ag-news/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://textattack/distilbert-base-uncased-ag-news/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/textattack/distilbert-base-uncased-ag-news/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 2a744d0871c86407d217f59fd1ffd4bf50701f8e23e075dad54e2e9ba79247bb
- Size of remote file:
- 268 MB
- SHA256:
- e37dae48918d11e0c8117ab6b6fb2f68df690352b8405f7383c921a40a017588
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