Instructions to use textattack/bert-base-uncased-ag-news with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-ag-news with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-ag-news")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-ag-news") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-ag-news", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from textattack/bert-base-uncased-ag-news: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/textattack/bert-base-uncased-ag-news/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://textattack/bert-base-uncased-ag-news/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/textattack/bert-base-uncased-ag-news/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- c4685e60354c98937f8c02683a4431076d417cfc4caa0136da47ab4ed8ec04c8
- Size of remote file:
- 438 MB
- SHA256:
- dc7ed11b084d7d1a9bbec20e4ed39caec163b51ca780aee4a100ae02ecea7ce4
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