Instructions to use Hartunka/distilbert_km_5_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/distilbert_km_5_v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DistilBertForLDAMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_km_5_v2") model = DistilBertForLDAMaskedLM.from_pretrained("Hartunka/distilbert_km_5_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/distilbert_km_5_v2: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/Hartunka/distilbert_km_5_v2/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/distilbert_km_5_v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/distilbert_km_5_v2/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- db56341b4d7f6a1a10e53b8c66d1d61f1d84cb125007189216a37b0c74b7b778
- Size of remote file:
- 5.43 kB
- SHA256:
- 9548f9ae701093c6a23810b1f0e798ecf80ddac985705a3e3dc2671724915488
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