Instructions to use YituTech/conv-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YituTech/conv-bert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="YituTech/conv-bert-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("YituTech/conv-bert-base") model = AutoModel.from_pretrained("YituTech/conv-bert-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from YituTech/conv-bert-base: direct link, hf CLI and curl.
- Browser
- Download file 423 MB
-
https://huggingface.co/YituTech/conv-bert-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YituTech/conv-bert-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/YituTech/conv-bert-base/resolve/main/pytorch_model.bin
423 MB
- Xet hash:
- 3015529ec3e990afd4e58896e6582933cf9559c9479ff8e116d8b68061d0688e
- Size of remote file:
- 423 MB
- SHA256:
- 12c7e75e7372edda8fdb5d12925b0042267d8d8517fce8fb8a010422dc71cc35
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