Instructions to use devansvd/bert-model-test-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devansvd/bert-model-test-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="devansvd/bert-model-test-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("devansvd/bert-model-test-2") model = AutoModelForTokenClassification.from_pretrained("devansvd/bert-model-test-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from devansvd/bert-model-test-2: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/devansvd/bert-model-test-2/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://devansvd/bert-model-test-2@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/devansvd/bert-model-test-2/resolve/refs%2Fpr%2F1/pytorch_model.bin
436 MB
- Xet hash:
- 720c786f2fbb45b7e9b7bf42f6889d88e22e12982abfac4e80ed9179f5093a25
- Size of remote file:
- 436 MB
- SHA256:
- ba8c3f80434488026a8add151a43b10d4e944d485e0981364daf579abe223c02
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.