Instructions to use Hartunka/bert_base_km_50_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_km_50_v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DistilBertForLDAMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_km_50_v1") model = DistilBertForLDAMaskedLM.from_pretrained("Hartunka/bert_base_km_50_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/bert_base_km_50_v1: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/Hartunka/bert_base_km_50_v1/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/bert_base_km_50_v1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/bert_base_km_50_v1/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 7eef4ae65d957bc49bb3206926c8161f2ed445f2747fac33b6b112b54bbdbd28
- Size of remote file:
- 5.43 kB
- SHA256:
- c7dc2860814051d3ee971585a582749ad85e90a43a57a88764d1a75d7305067e
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