Instructions to use Hartunka/bert_base_km_5_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_km_5_v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, DistilBertForLDAMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_km_5_v2") model = DistilBertForLDAMaskedLM.from_pretrained("Hartunka/bert_base_km_5_v2") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 25.0, | |
| "eval_accuracy": 0.15655195795897608, | |
| "eval_loss": 6.156774044036865, | |
| "eval_runtime": 1.0286, | |
| "eval_samples": 479, | |
| "eval_samples_per_second": 465.676, | |
| "eval_steps_per_second": 4.861, | |
| "perplexity": 471.9032775005862, | |
| "total_flos": 1.5149101316039424e+18, | |
| "train_loss": 6.119275358441895, | |
| "train_runtime": 24674.4736, | |
| "train_samples": 228639, | |
| "train_samples_per_second": 231.655, | |
| "train_steps_per_second": 2.413 | |
| } |