Instructions to use jenspt/bert_classification_27_04 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jenspt/bert_classification_27_04 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jenspt/bert_classification_27_04")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jenspt/bert_classification_27_04") model = AutoModelForSequenceClassification.from_pretrained("jenspt/bert_classification_27_04", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jenspt/bert_classification_27_04: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/jenspt/bert_classification_27_04/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jenspt/bert_classification_27_04/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jenspt/bert_classification_27_04/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 34364d47f1be3f5c57f740553b8043da8be78cac0e034193605f2b7814ec913d
- Size of remote file:
- 438 MB
- SHA256:
- 73dd04a4bbb4309751672d3a0a9b724a8ed06d1e72bb310df2018fae4a0c02f5
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