Question Answering
Transformers
PyTorch
JAX
Safetensors
English
bert
bert-base
Eval Results (legacy)
Instructions to use csarron/bert-base-uncased-squad-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use csarron/bert-base-uncased-squad-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="csarron/bert-base-uncased-squad-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("csarron/bert-base-uncased-squad-v1") model = AutoModelForQuestionAnswering.from_pretrained("csarron/bert-base-uncased-squad-v1") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 36e774ebe7f09550a909bfaf76051df06590da36b0e75919f9705ae01b072cbd
- Size of remote file:
- 1.64 kB
- SHA256:
- fe4cc9c9784edee24a2bf5b2ce4fb6d8411d0c56e16c2100bea793618cead40f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.