flan-t5-xl-squad2 / README.md
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metadata
language: en
license: cc-by-4.0
base_model: google/flan-t5-xl
tags:
  - question-answering
  - flan
  - flan-t5
  - squad
  - squad_v2
datasets:
  - squad_v2
  - squad
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 model, fine-tuned using the SQuAD2.0 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 QA pipeline on Haystack

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 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:

# NOTE: This only works with Haystack v2.0!
reader = ExtractiveReader(model_name_or_path="deepset/flan-t5-xl-squad2")

In Transformers

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

deepset is the company behind the open-source NLP framework Haystack which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.

Get in touch and join the Haystack community

For more info on Haystack, visit our GitHub repo and Documentation.

We also have a Discord community open to everyone!

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