Instructions to use Hartunka/bert_base_rand_50_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_rand_50_v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DistilBertForLDAMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_50_v1") model = DistilBertForLDAMaskedLM.from_pretrained("Hartunka/bert_base_rand_50_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/bert_base_rand_50_v1: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/Hartunka/bert_base_rand_50_v1/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/bert_base_rand_50_v1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/bert_base_rand_50_v1/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- c8f53ee878cd4e191e44a1818357fec5d0b2389287adb0bb71d315276e0be0cd
- Size of remote file:
- 5.43 kB
- SHA256:
- 35fbb3ec252b17630e93b77e5f063f11086309337387acc2589a9d2db032a901
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