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 eval_results.json from Yannis98/tmp_natqa: direct link, hf CLI and curl.
- Browser
- Download file 255 Bytes
-
https://huggingface.co/Yannis98/tmp_natqa/resolve/main/eval_results.json
- Command line
-
hf download hf://Yannis98/tmp_natqa/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/Yannis98/tmp_natqa/resolve/main/eval_results.json
255 Bytes
| { | |
| "eval_exact_match": 71.68120909940791, | |
| "eval_f1": 83.23616256996132, | |
| "eval_model_preparation_time": 0.0008, | |
| "eval_runtime": 1247.1447, | |
| "eval_samples": 22353, | |
| "eval_samples_per_second": 17.923, | |
| "eval_steps_per_second": 0.035 | |
| } |