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 eval_results.json from Jeska/BertjeWDialDataALL: direct link, hf CLI and curl.
- Browser
- Download file 223 Bytes
-
https://huggingface.co/Jeska/BertjeWDialDataALL/resolve/main/eval_results.json
- Command line
-
hf download hf://Jeska/BertjeWDialDataALL/eval_results.json
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curl -L -o eval_results.json https://huggingface.co/Jeska/BertjeWDialDataALL/resolve/main/eval_results.json
223 Bytes
| { | |
| "epoch": 8.0, | |
| "eval_loss": 1.946905255317688, | |
| "eval_runtime": 80.8062, | |
| "eval_samples": 5193, | |
| "eval_samples_per_second": 64.265, | |
| "eval_steps_per_second": 8.044, | |
| "perplexity": 7.006969210814188 | |
| } |