Instructions to use Hartunka/bert_base_rand_10_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_rand_10_v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DistilBertForLDAMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_10_v1") model = DistilBertForLDAMaskedLM.from_pretrained("Hartunka/bert_base_rand_10_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from Hartunka/bert_base_rand_10_v1: direct link, hf CLI and curl.
- Browser
- Download file 260 Bytes
-
https://huggingface.co/Hartunka/bert_base_rand_10_v1/resolve/main/eval_results.json
- Command line
-
hf download hf://Hartunka/bert_base_rand_10_v1/eval_results.json
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curl -L -o eval_results.json https://huggingface.co/Hartunka/bert_base_rand_10_v1/resolve/main/eval_results.json
260 Bytes
| { | |
| "epoch": 25.0, | |
| "eval_accuracy": 0.152963779171611, | |
| "eval_loss": 8.3502779006958, | |
| "eval_runtime": 1.0302, | |
| "eval_samples": 479, | |
| "eval_samples_per_second": 464.97, | |
| "eval_steps_per_second": 4.854, | |
| "perplexity": 4231.356476663692 | |
| } |