Instructions to use Kdogs/dapt_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kdogs/dapt_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Kdogs/dapt_bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Kdogs/dapt_bert") model = AutoModelForMaskedLM.from_pretrained("Kdogs/dapt_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Kdogs/dapt_bert: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/Kdogs/dapt_bert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Kdogs/dapt_bert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Kdogs/dapt_bert/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 3b38bd688025e68744dbc87e09b231b477e0683d0451e94b17bf7f48d63b133d
- Size of remote file:
- 443 MB
- SHA256:
- cf0e5dfc6f80698a8fea68228008805a97e30fbaac01caf7a7c1d14b0f1ac0d2
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