Instructions to use jenspt/bert_regression_basic_16_batch_size with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jenspt/bert_regression_basic_16_batch_size with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jenspt/bert_regression_basic_16_batch_size")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jenspt/bert_regression_basic_16_batch_size") model = AutoModelForSequenceClassification.from_pretrained("jenspt/bert_regression_basic_16_batch_size", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jenspt/bert_regression_basic_16_batch_size: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/jenspt/bert_regression_basic_16_batch_size/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jenspt/bert_regression_basic_16_batch_size/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jenspt/bert_regression_basic_16_batch_size/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 743ac1aaaa00235687a0221f9282406843be5b3cc68c3e14ee1095907413562c
- Size of remote file:
- 438 MB
- SHA256:
- da8f5b3832d2b7f1c747fdab37f2db94c694437fead4acfa2fe72258e4f938f9
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