Instructions to use Hartunka/bert_base_km_100_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_km_100_v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DistilBertForLDAMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_km_100_v2") model = DistilBertForLDAMaskedLM.from_pretrained("Hartunka/bert_base_km_100_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
End of training
Verified signaturedef1f9d -
Model save
Verified signature27c02e4 -
Training in progress, step 59550
Verified signaturee40a6ae -
Training in progress, step 50000
Verified signature76d8f72 -
Training in progress, step 40000
Verified signaturec686f02 -
Training in progress, step 30000
Verified signatured54cf4e
-
Training in progress, step 20000
Verified signatureb64d03a -
Training in progress, step 10000
Verified signatureef878f4
-
End of training
Verified signature23bd287 -
Model save
Verified signature2bdf1b7 -
Training in progress, step 59550
Verified signature2fddf12 -
Training in progress, step 50000
Verified signature8763b81 -
Training in progress, step 40000
Verified signature6fd64d3 -
Training in progress, step 30000
Verified signatured0baacf -
Training in progress, step 20000
Verified signature30da039 -
Training in progress, step 10000
Verified signaturedccb4e9 -
initial commit
Verified signature803408d