Instructions to use Hartunka/bert_base_rand_10_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_rand_10_v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DistilBertForLDAMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_10_v2") model = DistilBertForLDAMaskedLM.from_pretrained("Hartunka/bert_base_rand_10_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from Hartunka/bert_base_rand_10_v2: direct link, hf CLI and curl.
- Browser
- Download file 262 Bytes
-
https://huggingface.co/Hartunka/bert_base_rand_10_v2/resolve/main/eval_results.json
- Command line
-
hf download hf://Hartunka/bert_base_rand_10_v2/eval_results.json
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curl -L -o eval_results.json https://huggingface.co/Hartunka/bert_base_rand_10_v2/resolve/main/eval_results.json
262 Bytes
| { | |
| "epoch": 25.0, | |
| "eval_accuracy": 0.15282251229021868, | |
| "eval_loss": 8.3711576461792, | |
| "eval_runtime": 1.0257, | |
| "eval_samples": 479, | |
| "eval_samples_per_second": 466.98, | |
| "eval_steps_per_second": 4.875, | |
| "perplexity": 4320.634935200771 | |
| } |