Instructions to use jayavibhav/bert-classification-10ksamples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayavibhav/bert-classification-10ksamples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jayavibhav/bert-classification-10ksamples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jayavibhav/bert-classification-10ksamples") model = AutoModelForSequenceClassification.from_pretrained("jayavibhav/bert-classification-10ksamples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jayavibhav/bert-classification-10ksamples: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/jayavibhav/bert-classification-10ksamples/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jayavibhav/bert-classification-10ksamples/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jayavibhav/bert-classification-10ksamples/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- f5b44d778246d87ea7383f68490e489692b7a7105fb396cd16efbe64fc795f07
- Size of remote file:
- 438 MB
- SHA256:
- 4ca0ee8a49f435918edb3d2dfc7998b56367d0629777ef2f7a15fa792af0f47d
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