Instructions to use mcurmei/single_label_N_max_long_training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mcurmei/single_label_N_max_long_training with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="mcurmei/single_label_N_max_long_training")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mcurmei/single_label_N_max_long_training") model = AutoModelForQuestionAnswering.from_pretrained("mcurmei/single_label_N_max_long_training") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 67a47354ecbb89e80d7310cd56f9a56567e748deab1e8b5f985836da2bae342d
- Size of remote file:
- 3.12 kB
- SHA256:
- 00f46c5455aa58b0335a3948c7624744e67d7849bd489f9161ea398713a4e3de
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.