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 train_results.json from Hartunka/bert_base_rand_100_v2: direct link, hf CLI and curl.
- Browser
- Download file 239 Bytes
-
https://huggingface.co/Hartunka/bert_base_rand_100_v2/resolve/main/train_results.json
- Command line
-
hf download hf://Hartunka/bert_base_rand_100_v2/train_results.json
-
curl -L -o train_results.json https://huggingface.co/Hartunka/bert_base_rand_100_v2/resolve/main/train_results.json
239 Bytes
| { | |
| "epoch": 25.0, | |
| "total_flos": 1.5161929390646784e+18, | |
| "train_loss": 8.272559419851358, | |
| "train_runtime": 25341.6856, | |
| "train_samples": 228639, | |
| "train_samples_per_second": 225.556, | |
| "train_steps_per_second": 2.35 | |
| } |