Question Answering
Transformers
PyTorch
Safetensors
English
t5
flan
flan-t5
squad
squad_v2
Eval Results (legacy)
text-generation-inference
Instructions to use deepset/flan-t5-xl-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/flan-t5-xl-squad2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="deepset/flan-t5-xl-squad2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/flan-t5-xl-squad2") model = AutoModelForQuestionAnswering.from_pretrained("deepset/flan-t5-xl-squad2") - Notebooks
- Google Colab
- Kaggle
Commit 路
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Parent(s): cdf2718
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README.md
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Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do extractive question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/):
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```python
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# NOTE: This only works with Haystack v2.0!
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reader = ExtractiveReader(
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```
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### In Transformers
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Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do extractive question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/):
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```python
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# NOTE: This only works with Haystack v2.0!
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reader = ExtractiveReader("deepset/flan-t5-xl-squad2")
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```
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### In Transformers
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