Instructions to use Yannis98/tmp_natqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yannis98/tmp_natqa with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="Yannis98/tmp_natqa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Yannis98/tmp_natqa") model = AutoModelForQuestionAnswering.from_pretrained("Yannis98/tmp_natqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download predict_val_results.json from Yannis98/tmp_natqa: direct link, hf CLI and curl.
- Browser
- Download file 262 Bytes
-
https://huggingface.co/Yannis98/tmp_natqa/resolve/main/predict_val_results.json
- Command line
-
hf download hf://Yannis98/tmp_natqa/predict_val_results.json
-
curl -L -o predict_val_results.json https://huggingface.co/Yannis98/tmp_natqa/resolve/main/predict_val_results.json
262 Bytes
| { | |
| "predict_samples_val": 22353, | |
| "test_exact_match": 71.68120909940791, | |
| "test_f1": 83.23616256996132, | |
| "test_model_preparation_time": 0.0008, | |
| "test_runtime": 1249.0977, | |
| "test_samples_per_second": 17.895, | |
| "test_steps_per_second": 0.035 | |
| } |