Instructions to use jenspt/bert_regression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jenspt/bert_regression with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jenspt/bert_regression")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jenspt/bert_regression") model = AutoModelForSequenceClassification.from_pretrained("jenspt/bert_regression", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jenspt/bert_regression: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/jenspt/bert_regression/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jenspt/bert_regression/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jenspt/bert_regression/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- b94d6a5677e8b57708bacc2dd0a70927d9b177f3c6272dabb8e8213c090b9a59
- Size of remote file:
- 438 MB
- SHA256:
- e01be3b671fd2ec080783be2fa733a9ec49edbedea1b5817cc38ec04998886a1
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