Instructions to use Hartunka/tiny_bert_rand_100_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_rand_100_v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DistilBertForLDAMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_100_v1") model = DistilBertForLDAMaskedLM.from_pretrained("Hartunka/tiny_bert_rand_100_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from Hartunka/tiny_bert_rand_100_v1: direct link, hf CLI and curl.
- Browser
- Download file 238 Bytes
-
https://huggingface.co/Hartunka/tiny_bert_rand_100_v1/resolve/main/train_results.json
- Command line
-
hf download hf://Hartunka/tiny_bert_rand_100_v1/train_results.json
-
curl -L -o train_results.json https://huggingface.co/Hartunka/tiny_bert_rand_100_v1/resolve/main/train_results.json
238 Bytes
| { | |
| "epoch": 25.0, | |
| "total_flos": 3.058354515764736e+17, | |
| "train_loss": 9.145212555059759, | |
| "train_runtime": 12209.163, | |
| "train_samples": 228639, | |
| "train_samples_per_second": 468.171, | |
| "train_steps_per_second": 4.877 | |
| } |