Instructions to use Hartunka/bert_base_rand_100_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_rand_100_v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DistilBertForLDAMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_100_v2") model = DistilBertForLDAMaskedLM.from_pretrained("Hartunka/bert_base_rand_100_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Hartunka/bert_base_rand_100_v2: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/Hartunka/bert_base_rand_100_v2/resolve/main/tokenizer.json
- Command line
-
hf download hf://Hartunka/bert_base_rand_100_v2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Hartunka/bert_base_rand_100_v2/resolve/main/tokenizer.json
711 kB
File too large to display, you can check the raw version instead.