Instructions to use Hartunka/bert_base_km_20_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_km_20_v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DistilBertForLDAMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_km_20_v2") model = DistilBertForLDAMaskedLM.from_pretrained("Hartunka/bert_base_km_20_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
End of training
Verified signature065e127 -
Model save
Verified signature38134e9 -
Training in progress, step 59550
Verified signaturee579d90 -
Training in progress, step 50000
Verified signature931ab89 -
Training in progress, step 40000
Verified signature3a7f99c -
Training in progress, step 30000
Verified signature4b9e272 -
Training in progress, step 20000
Verified signatureff26f38 -
Training in progress, step 10000
Verified signature40def6a
-
End of training
Verified signatureefee11c -
Model save
Verified signature4fb260c -
Training in progress, step 59550
Verified signature62b383e -
Training in progress, step 50000
Verified signature43d8e9d -
Training in progress, step 40000
Verified signature327f87a -
Training in progress, step 30000
Verified signature743968c
-
Training in progress, step 20000
Verified signature652fcb0 -
Training in progress, step 10000
Verified signature1fd8aa2 -
initial commit
Verified signature012c9ca