Instructions to use ZZ99/deberta-v3-large-tapt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZZ99/deberta-v3-large-tapt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ZZ99/deberta-v3-large-tapt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ZZ99/deberta-v3-large-tapt") model = AutoModelForMaskedLM.from_pretrained("ZZ99/deberta-v3-large-tapt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download all_results.json from ZZ99/deberta-v3-large-tapt: direct link, hf CLI and curl.
- Browser
- Download file 438 Bytes
-
https://huggingface.co/ZZ99/deberta-v3-large-tapt/resolve/main/all_results.json
- Command line
-
hf download hf://ZZ99/deberta-v3-large-tapt/all_results.json
-
curl -L -o all_results.json https://huggingface.co/ZZ99/deberta-v3-large-tapt/resolve/main/all_results.json
438 Bytes
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.7284798913853338, | |
| "eval_loss": 1.3251452445983887, | |
| "eval_runtime": 99.5196, | |
| "eval_samples": 794, | |
| "eval_samples_per_second": 7.978, | |
| "eval_steps_per_second": 1.005, | |
| "perplexity": 3.762731831786301, | |
| "train_loss": 0.7688173565565709, | |
| "train_runtime": 16329.8249, | |
| "train_samples": 14828, | |
| "train_samples_per_second": 2.724, | |
| "train_steps_per_second": 0.681 | |
| } |