Instructions to use Jeska/BertjeWDialDataALL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeska/BertjeWDialDataALL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Jeska/BertjeWDialDataALL")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Jeska/BertjeWDialDataALL") model = AutoModelForMaskedLM.from_pretrained("Jeska/BertjeWDialDataALL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from Jeska/BertjeWDialDataALL: direct link, hf CLI and curl.
- Browser
- Download file 195 Bytes
-
https://huggingface.co/Jeska/BertjeWDialDataALL/resolve/main/train_results.json
- Command line
-
hf download hf://Jeska/BertjeWDialDataALL/train_results.json
-
curl -L -o train_results.json https://huggingface.co/Jeska/BertjeWDialDataALL/resolve/main/train_results.json
195 Bytes
| { | |
| "epoch": 8.0, | |
| "train_loss": 2.007555020303887, | |
| "train_runtime": 46638.2691, | |
| "train_samples": 98673, | |
| "train_samples_per_second": 16.926, | |
| "train_steps_per_second": 0.265 | |
| } |