Instructions to use deepset/quora_dedup_bert_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/quora_dedup_bert_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="deepset/quora_dedup_bert_base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("deepset/quora_dedup_bert_base") model = AutoModel.from_pretrained("deepset/quora_dedup_bert_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from deepset/quora_dedup_bert_base: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/deepset/quora_dedup_bert_base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://deepset/quora_dedup_bert_base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/deepset/quora_dedup_bert_base/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- 8de7267c77d1304f18d8665e0d848cd31eb502eae8b072e625d74ce90ef9b45c
- Size of remote file:
- 438 MB
- SHA256:
- 434af2a8888970ff6348667466f40fd421e378ad2e6020bb66433d2ce3657a55
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.