Instructions to use ccdv/lsg-bert-base-uncased-4096 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ccdv/lsg-bert-base-uncased-4096 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ccdv/lsg-bert-base-uncased-4096", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-bert-base-uncased-4096", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("ccdv/lsg-bert-base-uncased-4096", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ccdv/lsg-bert-base-uncased-4096: direct link, hf CLI and curl.
- Browser
- Download file 452 MB
-
https://huggingface.co/ccdv/lsg-bert-base-uncased-4096/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ccdv/lsg-bert-base-uncased-4096/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ccdv/lsg-bert-base-uncased-4096/resolve/main/pytorch_model.bin
452 MB
- Xet hash:
- 5c927ddf50354e00926ec81f73ba4890e1366bfd7270779aeeb20c71379c20d0
- Size of remote file:
- 452 MB
- SHA256:
- 672aec6c2b777de8fb689b8fbafa3efd5960a6dd897ad4070fee85f17cf12b96
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