Instructions to use Hartunka/bert_base_rand_20_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_rand_20_v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DistilBertForLDAMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_20_v1") model = DistilBertForLDAMaskedLM.from_pretrained("Hartunka/bert_base_rand_20_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/bert_base_rand_20_v1: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/Hartunka/bert_base_rand_20_v1/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/bert_base_rand_20_v1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/bert_base_rand_20_v1/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- d57fe559ee4beb0610f85e00780a4275372fea5a0943158eb5d27203f8063bc2
- Size of remote file:
- 5.43 kB
- SHA256:
- f7bec27aace516a6b53c2d07819614db8366d1cdc9339ea50deb75b89af74b54
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