Instructions to use mrm8488/bert-mini-finetuned-squadv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/bert-mini-finetuned-squadv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="mrm8488/bert-mini-finetuned-squadv2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mrm8488/bert-mini-finetuned-squadv2") model = AutoModelForQuestionAnswering.from_pretrained("mrm8488/bert-mini-finetuned-squadv2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd3a80cb8dbe18eee49a147800e3ea9ecb2f1a9d61bc3e577d2e36be70bba423
- Size of remote file:
- 44.4 MB
- SHA256:
- bb980d6d79ae730ded9377b7af57377956f9f20e58122fe840d4c0fe7c7b6b59
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.