Instructions to use Hartunka/distilbert_rand_20_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/distilbert_rand_20_v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DistilBertForLDAMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_rand_20_v2") model = DistilBertForLDAMaskedLM.from_pretrained("Hartunka/distilbert_rand_20_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/distilbert_rand_20_v2: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/Hartunka/distilbert_rand_20_v2/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/distilbert_rand_20_v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/distilbert_rand_20_v2/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 67623d51c412b483434101fd3887fbeff56654f0405749f898a73d8b260a4889
- Size of remote file:
- 5.43 kB
- SHA256:
- df6bfe8b07763872cfe02b34a6d60fcc7d7fbfed3ad27e722446f4d9d4c72666
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