Instructions to use ToluClassics/extractive_reader_nq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ToluClassics/extractive_reader_nq 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="ToluClassics/extractive_reader_nq")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ToluClassics/extractive_reader_nq") model = AutoModelForQuestionAnswering.from_pretrained("ToluClassics/extractive_reader_nq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download all_results.json from ToluClassics/extractive_reader_nq: direct link, hf CLI and curl.
- Browser
- Download file 503 Bytes
-
https://huggingface.co/ToluClassics/extractive_reader_nq/resolve/main/all_results.json
- Command line
-
hf download hf://ToluClassics/extractive_reader_nq/all_results.json
-
curl -L -o all_results.json https://huggingface.co/ToluClassics/extractive_reader_nq/resolve/main/all_results.json
503 Bytes
| { | |
| "eval_HasAns_exact": 2.580971659919028, | |
| "eval_HasAns_f1": 3.6059639134517205, | |
| "eval_HasAns_total": 5928, | |
| "eval_NoAns_exact": 67.68713204373422, | |
| "eval_NoAns_f1": 67.68713204373422, | |
| "eval_NoAns_total": 5945, | |
| "eval_best_exact": 50.07159100480081, | |
| "eval_best_exact_thresh": 0.0, | |
| "eval_best_f1": 50.07159100480081, | |
| "eval_best_f1_thresh": 0.0, | |
| "eval_exact": 35.18066200623263, | |
| "eval_f1": 35.69242433074553, | |
| "eval_samples": 12204, | |
| "eval_total": 11873 | |
| } |