transformers pytorch safetensors t5 question-answering flan flan-t5 squad squad_v2 en dataset:squad_v2 dataset:squad base_model:google/flan-t5-xl base_model:finetune:google/flan-t5-xl license:cc-by-4.0 model-index text-generation-inference endpoints_compatible region:us
---
base_model: google/flan-t5-xl
datasets:
- squad_v2
- squad
language: en
license: cc-by-4.0
tags:
- question-answering
- flan
- flan-t5
- squad
- squad_v2
model-index:
- name: deepset/flan-t5-xl-squad2
  results:
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squad_v2
      type: squad_v2
      config: squad_v2
      split: validation
    metrics:
    - type: exact_match
      value: 88.79
      name: Exact Match
    - type: f1
      value: 91.617
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squad
      type: squad
      config: plain_text
      split: validation
    metrics:
    - type: exact_match
      value: 90.331
      name: Exact Match
    - type: f1
      value: 95.722
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: adversarial_qa
      type: adversarial_qa
      config: adversarialQA
      split: validation
    metrics:
    - type: exact_match
      value: 54.367
      name: Exact Match
    - type: f1
      value: 68.055
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squad_adversarial
      type: squad_adversarial
      config: AddOneSent
      split: validation
    metrics:
    - type: exact_match
      value: 87.241
      name: Exact Match
    - type: f1
      value: 92.894
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squadshifts amazon
      type: squadshifts
      config: amazon
      split: test
    metrics:
    - type: exact_match
      value: 77.602
      name: Exact Match
    - type: f1
      value: 90.426
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squadshifts new_wiki
      type: squadshifts
      config: new_wiki
      split: test
    metrics:
    - type: exact_match
      value: 85.639
      name: Exact Match
    - type: f1
      value: 93.974
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squadshifts nyt
      type: squadshifts
      config: nyt
      split: test
    metrics:
    - type: exact_match
      value: 87.392
      name: Exact Match
    - type: f1
      value: 94.579
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squadshifts reddit
      type: squadshifts
      config: reddit
      split: test
    metrics:
    - type: exact_match
      value: 79.323
      name: Exact Match
    - type: f1
      value: 90.083
      name: F1
---
# flan-t5-xl for Extractive QA 

This is the [flan-t5-xl](https://huggingface.co/google/flan-t5-xl) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering. 


## Overview
**Language model:** flan-t5-xl  
**Language:** English  
**Downstream-task:** Extractive QA  
**Training data:** SQuAD 2.0  
**Eval data:** SQuAD 2.0  
**Code:**  See [an example extractive QA pipeline built with Haystack](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline)  

## Hyperparameters

```
learning_rate: 1e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 4.0
``` 

## Usage

### In Haystack
Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. 
To load and run the model with [Haystack](https://github.com/deepset-ai/haystack/):
```python
# After running pip install haystack-ai "transformers[torch,sentencepiece]"

from haystack import Document
from haystack.components.readers import ExtractiveReader

docs = [
    Document(content="Python is a popular programming language"),
    Document(content="python ist eine beliebte Programmiersprache"),
]

reader = ExtractiveReader(model="deepset/flan-t5-xl-squad2")
reader.warm_up()

question = "What is a popular programming language?"
result = reader.run(query=question, documents=docs)
# {'answers': [ExtractedAnswer(query='What is a popular programming language?', score=0.5740374326705933, data='python', document=Document(id=..., content: '...'), context=None, document_offset=ExtractedAnswer.Span(start=0, end=6),...)]}
```
For a complete example with an extractive question answering pipeline that scales over many documents, check out the [corresponding Haystack tutorial](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline).

### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "deepset/flan-t5-xl-squad2"

# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
    'question': 'Why is model conversion important?',
    'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
}
res = nlp(QA_input)

# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```

## Authors
**Sebastian Husch Lee:** sebastian.huschlee [at] deepset.ai

## About us

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