Instructions to use jenspt/bert_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jenspt/bert_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jenspt/bert_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jenspt/bert_classification") model = AutoModelForSequenceClassification.from_pretrained("jenspt/bert_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jenspt/bert_classification: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/jenspt/bert_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jenspt/bert_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jenspt/bert_classification/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 05e56902569f9ec0dfdae0fbab081aefff5ab5b53b5544aeec615330376251f0
- Size of remote file:
- 438 MB
- SHA256:
- b1295be2554073d690321aca95c2d395ba512ece84b1aa16723db17f56d46b20
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