Instructions to use VCNC/bert_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VCNC/bert_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VCNC/bert_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VCNC/bert_2") model = AutoModelForSequenceClassification.from_pretrained("VCNC/bert_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from VCNC/bert_2: direct link, hf CLI and curl.
- Browser
- Download file 346 MB
-
https://huggingface.co/VCNC/bert_2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://VCNC/bert_2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/VCNC/bert_2/resolve/main/pytorch_model.bin
346 MB
- Xet hash:
- 91c1fe2b095d8ae39dcc84a545d661bf4eddaea8ae4bbb025cfe93f52a999d81
- Size of remote file:
- 346 MB
- SHA256:
- 1e521a0a66b3e5635c19f605cf8ae6ff62f562aef85269cb35e65e2ebeaeed2d
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