Instructions to use Hartunka/distilbert_rand_10_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/distilbert_rand_10_v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DistilBertForLDAMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_rand_10_v2") model = DistilBertForLDAMaskedLM.from_pretrained("Hartunka/distilbert_rand_10_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/distilbert_rand_10_v2: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/Hartunka/distilbert_rand_10_v2/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/distilbert_rand_10_v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/distilbert_rand_10_v2/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- c96094e751c4dfd32cc4bb21e424bf2133170437a2e8741a7113e3923e3af37f
- Size of remote file:
- 5.43 kB
- SHA256:
- de3dffeed0b7585c707ef43d73a42c7fa35463c586ccc1b4c05a9bc075977eb9
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