Question Answering
Transformers
TensorFlow
ONNX
Safetensors
distilbert
generated_from_keras_callback
Instructions to use wdavies/extract-answer-from-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wdavies/extract-answer-from-text with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="wdavies/extract-answer-from-text")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("wdavies/extract-answer-from-text") model = AutoModelForQuestionAnswering.from_pretrained("wdavies/extract-answer-from-text", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a6f1e93f3fe6b051a08727ac751692bdda6f3d3b8316208bd96d131d30c704f
- Size of remote file:
- 431 MB
- SHA256:
- 1341ffbac3437167d45e5bfcaaed26b875f4924841129b180e4b3d4900f19cdf
路
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