Instructions to use ToluClassics/extractive_reader_nq_squad_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ToluClassics/extractive_reader_nq_squad_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="ToluClassics/extractive_reader_nq_squad_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ToluClassics/extractive_reader_nq_squad_v2") model = AutoModelForQuestionAnswering.from_pretrained("ToluClassics/extractive_reader_nq_squad_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from ToluClassics/extractive_reader_nq_squad_v2: direct link, hf CLI and curl.
- Browser
- Download file 197 Bytes
-
https://huggingface.co/ToluClassics/extractive_reader_nq_squad_v2/resolve/main/train_results.json
- Command line
-
hf download hf://ToluClassics/extractive_reader_nq_squad_v2/train_results.json
-
curl -L -o train_results.json https://huggingface.co/ToluClassics/extractive_reader_nq_squad_v2/resolve/main/train_results.json
197 Bytes
| { | |
| "epoch": 5.0, | |
| "train_loss": 0.8785118254506847, | |
| "train_runtime": 11833.1715, | |
| "train_samples": 132115, | |
| "train_samples_per_second": 55.824, | |
| "train_steps_per_second": 0.873 | |
| } |