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
Commit History
-
End of training
Verified signature46f2ef8 -
Model save
Verified signaturea9dae29 -
Training in progress, step 59550
Verified signature049bb3e -
Training in progress, step 50000
Verified signaturecaf52ca -
Training in progress, step 40000
Verified signatureb092863 -
Training in progress, step 30000
Verified signaturef7e14de -
Training in progress, step 20000
Verified signaturec440d95 -
Training in progress, step 10000
Verified signature1b70d1d
-
Training in progress, step 10000
Verified signature5a16c8a -
initial commit
Verified signaturec48cdf2