Instructions to use bill/bert_finetuning_test1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bill/bert_finetuning_test1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="bill/bert_finetuning_test1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("bill/bert_finetuning_test1") model = AutoModelForMaskedLM.from_pretrained("bill/bert_finetuning_test1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download MRPC/pytorch_model.bin from bill/bert_finetuning_test1: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/bill/bert_finetuning_test1/resolve/main/MRPC/pytorch_model.bin
- Command line
-
hf download hf://bill/bert_finetuning_test1/MRPC/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bill/bert_finetuning_test1/resolve/main/MRPC/pytorch_model.bin
438 MB
- Xet hash:
- a4898503b726bb460253c7b53c0821f7b2dd8e92752a7cb6d149a2c1a72e12ed
- Size of remote file:
- 438 MB
- SHA256:
- caba1cd287b757a27acbb24da1af5f805e562285e56333b97b25e58cd2bb24b5
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