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("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.
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